Alternative measurement systems for recording cardiac activity in animals: a pilot study

Radana Kahankova; Jakub Kolarik; Jindřich Brablik; Katerina Barnova; Ivana Simkova; Radek Martinek

doi:10.1186/s40317-022-00286-y

Alternative measurement systems for recording cardiac activity in animals: a pilot study

Kahankova, Radana; Kolarik, Jakub; Brablik, Jindřich; Barnova, Katerina; Simkova, Ivana; Martinek, Radek 2022-04-27 00:00:00 Monitoring and assessing cardiac activity in animals, especially heart rate variability, has been gaining importance in the last few years as an indicator of animal health, well-being and physical condition. This pilot study tested the sen- sors based on ballistocardiography sensing the mechanical vibrations caused by the animal’s cardiovascular system, which have proved useful in measuring cardiac activity in humans. To verify the accuracy of these measurement sys- tems, the conventional measurements based on electrocardiography were carried out and the outcomes were com- pared. The main objectives were to verify the suitability of these sensors in measuring cardiac activity in animals, to determine the advantages and disadvantages of these sensors, and to identify future challenges. Measurements were performed on various animals, specifically a goat, a cow, a horse, and a sheep. Electrocardiographic measurement, which has demonstrated high accuracy in procedures for animals, was used as the study’s gold standard. A disadvan- tage of this method, however, is the long time required to prepare animals and shear spots to attach electrodes. The accuracy of a ballistocardiographic sensor was compared to reference electrocardiographic signals based on Bland– Altman plots which analysed the current heart rate values. Unfortunately, the ballistocardiographic sensor was highly prone to poor adhesion to the animal’s body, sensor movement when the animal was restless, and motion artefacts. Ballistocardiographic sensors were shown only to be effective with larger animals, i.e., the horse and the cow, the size of these animals allowing sufficient contact of the sensor with the animal’s body. However, this method’s most signifi- cant advantage over the conventional method based on electrocardiography is lower preparation time, since there is no need for precise and time-demanding fixation of the sensor itself and the necessity of shaving the animal’s body. Keywords: Animal electrocardiography (ECG), Heart rate variability (HRV ), Heart rate (HR), Animal welfare, Stress, Veterinary monitoring, Ballistocardiography (BCG), Farm animals in 1819 by French doctor René–Théophile–Hyacinthe Introduction Laënnec [1]. However, up to the early 1950s, only a few Monitoring cardiac activity in animals is currently an articles had discussed monitoring cardiac activity in important tool in the assessment of an animal’s over- horses and other animals, see [2]. A great change came in all health. The first mention of the potential diagnos - the 1960s, when David K. Detweiler, known as the “father tic importance of heart sounds in animals was written of veterinary cardiology”, brought animal cardiology to the forefront of veterinary practice [1]. Detweiler focused mainly on cardiovascular diseases in dogs, horses and *Correspondence: radana.kahankova@vsb.cz Department of Cybernetics and Biomedical Engineering, VSB— cattle [2–7]. He examined animals with systemic auscul- Technical University of Ostrava, Ostrava, Czechia tation and electrocardiography (ECG). Over time, special Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 2 of 21 cardiologic procedures were introduced and recognised the animal, applying gel or otherwise address the con- globally, and animal cardiology became an essential part ductivity between the electrode and the skin. Moreo- of veterinary practice [1]. ver, when acquiring the ECG signal, several electrodes Over the last few years, clinical assessment of cardiac need to be placed in different locations of the animal’s activity in animals has been gaining importance, not body, whereas to obtain BCG signal, a single sensor can only with regard to physiological and pathophysiological be used. When incorporated into a saddle or belt, this indicators, but also as an indicator of an animal’s men- method could offer a quick, simple, and durable attach - tal health and well-being [8–13]. The stress levels can be ment to the animal and become an ideal tool for cardiac measured using different physiological indicators, such monitoring in animals. This paper, therefore, aims to test as heart rate (HR) or serum levels of various stress hor- a prototype of the BCG-based measuring system to eval- mones (e.g., cortisol) [14–16]. The advantage of using uate its accuracy and suitability for this task. the HR (or heart rate variability, HRV) is that it can be monitored non-invasively and continually. The other Background mentioned methods require the blood to be collected Developments in sensing technology have made it pos- repetitively which is another stress factor for animals and sible to accurately monitor vital signs in humans, but in can lead to distorted results [10]. Thus, recording HR as the case of animals, measurement is more complicated. an indicator of stress seems to be a better option. This is mainly due to the different body structure and The HR assessment is used to identify stress in animals unpredictability of their behaviour. By monitoring the based on the assumption that HR reflects the activity of vital functions of animals, it is possible to obtain a large the sympathetic nerve fibres. However, changes in HR amount of valuable information about their physical are not exclusively in response to stress, i.e., a negative and mental health. Ensuring the welfare of animals and or painful stimulus, but also in response to other behav- preventing the occurrence of stress is associated with ioural and physiological influences, such as low oxygen their improved performance and thus positive economic levels [11], physical activity, temperature [12], illness [37, impact [24–36]. The goal of the researchers is, therefore, 38], and overall welfare of the animal [13]. Therefore, to design and put into practice an automated, non-inva- interpretation of mental state based solely on HR meas- sive system enabling long-term monitoring of animals. urement is not always unambiguous [17]. The HRV may This section summarizes examples of the practical use be a suitable alternative as a quantitative indicator of ani- of measuring animal cardiac activity, including monitor- mal health and well-being. This parameter represents the ing techniques and evaluation parameters that have been comprehensive degree of a physiological function derived used in the past. from the heart cycle. It expresses the variability which exists between consecutive heart rhythms and is meas- Cardiac monitoring in animals ured from the R wave of the QRS complex, which allows Changes in cardiac activity can be assessed according to more accurate and detailed assessment of the physical or numerous parameters with regard to time or frequency. mental condition of the animal [18–22]. In the time domain, the immediate HR can be deter- The HR can be measured by various techniques for mined at any point in time or in the intervals between cardiac activity monitoring, see section II.B. Among the consecutive QRS complexes, i.e., the normal-to-normal most commonly used methods is electrocardiography, (NN) intervals [20, 21]. The average NN interval, the which is considered a gold standard in cardiac monitor- average HR, or the difference between the longest and ing in humans. However, this method faces multiple chal- the shortest NN intervals are also simple parameters lenges in animal monitoring, especially due to presence which can be determined in the time domain. More of fur and differences in their anatomy, see section II.C. complex parameters can be calculated according to the Therefore, this article presents an alternative approach immediate HR, the direct measurement of NN intervals, in measuring HR, so-called ballistocardiography (BCG), or the differences between NN intervals [20]. Several which is based on monitoring body movements caused by studies [22–37] have reported that HRV or HR param- accelerated blood flow inside large vessels. The method eters are objective indicators of the mental and physical proved to be efficient in HR monitoring in humans and is well-being of animals during veterinary and breeding considered low-cost, simple to prepare, and easy to oper- practices. Examples of specific uses of measurements of ate [23]. HRV or HR parameters in animals are summarised in the For cardiac monitoring in animals, the BCG method following subsection and in Table 1. provides several benefits over the conventional ECG- The effect of the environment and the presence of based approach. The BCG has lower requirements on humans on HR and HRV parameters in animals (cows surface quality than ECG as it does not require shaving and horses) was investigated in [19, 24–30]. In cows, K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 3 of 21 Table 1 Examples of the use of HRV or HR measurement in animals Author, source Animal Use Results Rushen et al. [24] Cattle Impact of the environment and human presence Higher HR and lower milk production in isolate cows on the quantity of produced milk Rushen et al. [25] Cattle Impact of human behaviour in presence of cows Higher HR and lower milk production, if the cows on the quantity of produced milk were in the presence of a person who behaved aversively Kovacs et al. [37] Cattle Assessment of health (lameness) Lower HR and higher HRV in cows with lameness Stubsjøen et al. [38] Sheep Assessment of health (lameness) Higher HRV in sheep with lameness Mesangeau et al. [44] Miniature pigs Model for early detection of cardiovascular auto- Higher resting HR and lower HRV in pigs with cardio- nomic neuropathy vascular autonomic neuropathy Voss et al. [45] Pigs Model for sudden infant death syndrome Lower HRV in pigs during external warming Guidi et al. [19] Horse Estimation of the human–horse interaction for Quantitative measure of the human-horse interac- equine assisted therapy tion using HRV is viable Perkins et al. [40] Horse Assessment of health (grass sickness) Lower HRV in horses with grass sickness Rietmann et al. [41] Horse Assessment of pain after short-term and long-term Decrease of high-frequency component of HRV is treatment of laminitis attributable to an increase in pain Kuwahara et al. [42] Horse Assessment of health (atrial fibrillation) Higher low-frequency and high-frequency power of HRV in horses with atrial fibrillation McConachie et al. [43] Horse Assessment of health (acute gastrointestinal Lower HRV in horses with acute gastrointestinal disease) disease Janczarek et al. [47] Horse Impact on racing performance Higher HRV, better racing results Munsters et al. [49] Horse Impact of police training on welfare No significant changes in HR and HRV were detected, training is not stressful for both experi- enced and inexperienced horses Voss et al. [48] Horse Assessment o Rise of HR and decrease of HRV was observed with f degree of load during training increasing burden level König von Borstel et al. [30] Horse Temperament test HR and HRV were lowest when the horses were led and/or no human was present Nagel et al. [39] Horse Monitoring progress of delivery Higher mother’s HR immediately before delivery, higher HRV only during delivery these parameters were most frequently monitored in stress to the animal, this can be also assessed by the HR connection with the volume of milk the animals pro- and HRV parameters [49]. duce [24–27]. The results of the studies show that the Further investigations were focused on monitoring stress caused by the change of environment, such as animals’ well-being, especially early detection of illness their transfer to isolated stable [24], or adverse human or pain in cattle, sheep and horses. The research top - behaviour [25], lead to increase in HR and reduction ics covered, for example, relation of HR changes caused milk yield. Human–horse interaction can be estimated by stress due to lameness in cow [37] and sheep [38], through the assessment of HRV, since the presence of changes of HRV during labor in mares [39], or varia- rider or handler affects the horses’ inner behaviour tions of HRV in horses with diseases, such as grass [19, 28–30]. These findings can be applied in equine- sickness [40], laminitis [41], atrial fibrillation [42], or assisted therapy or in monitoring the effect of thera - gastrointestinal disease treated by exploratory lapa- peutic horseback riding [30]. rotomy [43]. The findings show that HRV can be used The same parameters (HRV and HR) were monitored to differentiate between healthy and sick animals [40, in horses to improve the quality of training and improve 42] but also assess presence of pain [41] and severity of the performance of the animals [47–49]. In terms of the illness [38], since its development is associated with performance in racing horses, the higher values of HRV significant increase in the HRV parameters [38]. Fur - parameters, the better results [47]. The training can thermore, a reduction of HRV after surgery was used to be thus adjusted (by, e.g., selecting a proper exercise) predict a non-survival and thus HRV can be used both according to the parameters and the aerobic work load for diagnostic and prognostic purposes [43]. As sug- needed for a given horse [48]. Moreover, for the train- gested in [41], such HRV-based indicator would be con- ing to be effective, it should not induce an unnecessary sidered a more practical and less expensive assessment Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 4 of 21 than other available tools, such as stress hormone analysis. Measurements of HRV and HR parameters in animals were also used in research that aimed to model human ECG diseases [44–46]. Several studies used pigs to model dis- Q S eases, such as sudden infant death syndrome [45], or cardiovascular autonomic neuropathy in subjects with L BCG diabetes [44]. The monitored HRV parameters showed significant changes correlating with symptoms of the K investigated diseases and HRV has thus proved as a suit- able method for early detection and therapeutic strategy development [44, 45]. Besides the HR evaluation, it is possible to analyze PPG the cardiac activity in animals using more sophisticated S1 S2 parameters. For example, in [50], the authors focused on evaluating the cardiac activity in calves using the RR PCG interval mean, RR interval standard deviation (SDRR), and root mean square of successive differences (RMSSD) Fig. 1 Example of ECG, BCG, PPG, and PCG waveforms. Compared to parameters in the time domain. The spectral band power the R peak in ECG, the other signals are physiologically delayed by the (VLF, LF, HF) and the LF/HF ratio were calculated in the pulse transit time [81] frequency domain. In [51], the mean interbeat interval, RMSSD, and pNN50 parameters were studied in horses. ods use, for example, a piezoelectric crystal placed on In [37], HRV in dairy cows was measured as an indica- the head of a metal shaft which contacts a membrane tor of chronic stress caused by lameness. The animals [67], principle of induction [68], or non-invasive were assessed according to RMSSD, HF, LF/HF, geomet- fibre-optics [69]. The PCG method was used for the ric measurements (triangular interpolation of normal to HR monitoring in dogs [53], horses [54, 55], and to normal, R–R triangular index), Poincaré measurements, monitor pregnancy in cattle [56]. and non-linear measurements (Shannon entropy, short- b) Photoplethysmography—uses a light source and a term fluctuations in HRV, long-term fluctuations in HRV, photodetector to measure variations of volume in correlation dimension). A more detailed overview of blood circulation [70]. A light source shines light HRV parameters can be found in [20, 21, 52]. into tissue and a photodetector then measures the quantity of reflected light, which is proportional Acquisition of cardiac activity to any change in blood volume. The most common Electrocardiography (ECG), ballistocardiography (BCG), PPG sensors use infrared LEDs or green LEDs as the photoplethysmography (PPG) and phonocardiography main light source [70, 71]. In veterinary medicine (PCG) are among the most frequently used techniques and research, the PPG was tested in guided pulse which have proved effective in monitoring cardiac activ - checks during cardiopulmonary resuscitation [57], or ity in humans. An example of all four curves is shown in to diagnose cardiovascular diseases in domestic ani- Fig. 1. The ECG-based monitoring is the most common mals [58]. The use of PPG for continuous monitoring method in both human and animal cardiac monitoring, of cardiac activity was assessed in dogs and cats [59], but only limited number of studies [53–64] have explored farm animals [60] and stress monitoring in sheep the application of the alternative methods in measuring during transport [61]. Moreover, besides external the cardiac activity of animals. The basic principles of the PPG sensors, there are also internal sensors available, individual techniques and their practical use in veteri- which enable continual subcutaneous data collection. nary can be summarised as follows: For example, the authors in [62] used an implantable but extravascular sensor for measuring blood oxygen a) Phonocardiography—a passive, low-cost method and saturation in sheep. one of the oldest techniques for recording the sounds c) Ballistocardiography—method based on capturing of the heart. The method captures the heart sounds the body movements caused by accelerated blood produced by the opening and closing of the heart flow inside large vessels [72]. Various types of sen - valves and blood flow [65]. The simplest method of sors can be used to capture these movements and are capturing heart sounds is in the use of a microphone able to generate a voltage as a result of mechanical placed on the surface of the body [66]. Other meth- K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 5 of 21 deformation or changes in pressure. For example, a these specialized devices is an obstacle in broader use in piezopolymer film pressure sensors [73, 74], elec- research. tromechanical film-based sensors [75, 76], hydraulic Many authors [82–95] have, therefore, opted for the use sensors [77, 78], hydraulic sensors or fibre optic sen - of ECG-based HR monitors in researching animal HRV, sors [79, 80]. So far, BCG measurements were tested since these are more affordable. These devices record the in domesticated animals [63] and in dogs [64]. times between the two main depolarisation waves (R–R d) Electrocardiography—captures the electric poten- intervals) and then convert these data into HR values. tials produced by the heart that are projected on the HR monitors produced by Polar Electro Oy (Kempele, body’s surface using electrodes placed on the skin. Finland) are frequently used. The Polar S810i model was This method is well known and used as a gold stand - used, for example in [82, 85–89]; the Polar Vantage NV ard in medicine. However, this technique faces many model was used in [93]; the Polar Sport Tester monitor obstacles when measured in animals, such as rela- was used in [13, 95]; and the Polar RS800 monitor was tively demanding preparation and low quality of the used in [37, 38]. However, these devices are not capable ECG signal due to numerous artefacts. This will be of recording all aspects of cardiac activity, and this leads discussed in detail in following section. to a loss of clinically important information which can otherwise be obtained using ECG. The absence of any gold standard for measurement and electrode placement is one of the major obstacles in cap- Standard monitoring and its challenges turing ECG in animals. Measurement using base-apex The ECG based monitoring requires relatively demand - leads (the electrodes are placed along the mean electri- ing preparation in animals [82]. First, an optimal location cal axis; one electrode on the lower thorax between the to fix the electrodes must be identified to prevent move - elbow and xiphoid and a second in the region between ment or removal of the electrodes; the selected locations the lower neck and the withers), while the animal stands on the animal’s body then need to be shaved. A disadvan- is performed most frequently in cattle, goats, sheep, and tage is that the quality of the ECG signal is often dete- horses [92, 94–96]. Examples of electrode placement riorated by artefacts recorded simultaneously with the during ECG capture are summarised below (Table 2 and useful signal. When ECG data is measured in an animal, Fig. 2). the motion artefacts are mainly caused by the animal’s With the use of the base-apex lead, ECG monitor- restlessness, since the measurement procedure itself is ing was performed on cows in [95]. ECG was captured rather stressful to animals [83, 84]. Additional process- using disposable adhesive electrodes and gel. The nega - ing of the ECG signal requires suitably selected filtering tive electrode was placed in the caudal angle of the left methods to obtain as precise information about the ani- scapula; the right electrode was placed in the left inter- mal’s health as possible. Recording for at least 5 min dur- costal region caudally to the olecranon; and the ground ing stationary conditions is recommended for adequate electrode was placed in the region of the left paralumbar analysis of HRV [20]. fossa. Similarly, the base-apex lead was also successfully Many devices for short-term and long-term monitor- used in [96] for ECG-based monitoring of sheep. Alli- ing exist for the recording, storage and analysis of human gator-type electrodes attached to the skin were used for ECG data. Unfortunately, this is relatively costly equip- measurement. The negative electrode was attached to ment adapted to the analysis of human cardiac activity the left side of the neck in the jugular furrow area; the [22]. There are only a few commercially available external positive electrode was positioned at the fifth intercostal ECG monitoring devices designed for use in veterinary space; and the ground electrode was placed away from medicine on the market. These include, for example, the these two electrodes. Televet 100 ECG monitor produced by Engel Engineer- In [50], the ECG was measured with the use of adhe- ing Service GmbH (Heusenstamm, Germany), which sive ECG electrodes attached to the shaved skin of a calf. can be used for continuous ECG monitoring in large and Secured with a band of elastic, one electrode was placed small animals. Another is the 6-channel veterinary ECG along the sternum, and the second was placed above the ek3008monitor with connection to the smartphone is right scapula. In [97], the authors compared the base- provided by Chip Ideas Electronics, S.L. eKuore (Valen- apex lead method and the Dubois method of monitor- cia, Spain). The Veterinary ECG/Heart Monitor Universal ing cardiac activity in horses. Placement of the four ECG Adapter manufactured by Woodley Equipment Company electrodes in the Dubois method was identified as a more Ltd (Bolton, UK) enabling wireless communication with a precise and reliable option. In this case, one electrode mobile application or the 3-channel veterinary ECG sys- was placed on the left scapula, the second electrode on tem ECG-T3V is manufactured by Shinova Medical Co., the right scapula, the third in the region of the sternum Ltd (Shanghai, China). However, higher purchase price of Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 6 of 21 Table 2 Summary of ECG electrode placement for the measurement of cardiac activity in animals Author, source Animal Electrode placement system Number of ECG electrode placement electrodes Hopster et al. [13] Cattle – 3 Electrodes placed on the left front leg, left part of the abdomen, and left hind leg Konold et al. [95] Cattle Base-apex 3 Negative electrode: left scapula, positive electrode: left intercostal space ground, electrode: left paralumbar fossa Depres et al. [50] Cattle – 2 Electrodes placed along the sternum and on the right scapula Tajik et al. [96] Sheep Base-apex 3 The negative electrode: left side of the neck on the jugular furrow, positive electrode: fifth intercostal space, ground electrode: away from the other two electrodes Guidi et al. [19] Horse Modified base-apex 2 Electrodes integrated into an elastic band and placed in the area behind Lanata et al. [83] the left front leg Lanata et al. [84] Lenoir et al. [57] Horse – 5 The positive electrode: left side of the thorax behind the olecranon, nega- Zucca et al. [88] tive electrode: left side of the chest behind the withers, ground electrode: above the left mid-thorax between these electrodes Costa et al. [97] Horse Dubois method 4 Measuring electrodes: right scapula, left scapula, region of the sternum (over the xiphoid); ground electrode: left front leg Rightside Leftside Leftside Left side GND GND 2 2 c) d) a) b) Leftside Leftside Rightside Left side 3 12 GND 2 2 4 GND e) f) g) Fig. 2 Example of ECG electrode placement for the measurement of cardiac activity in animals according to: a Hopster et al. [13], b Konold et al. [95], c Depres et al. [50], d Tajik et al. [96], e Guidi et al. [19], f Zucca et al. [88], and g Costa et al. [97] (over the xiphoid), and the ground electrode was posi- of elastic and two textile electrodes integrated into the tioned on the left front leg. Finally, in [57] and [88], the belt. Both electrodes were placed in the area behind the ECG was acquired using five electrodes in horses. The left front leg (modified base-apex system). In [19], the positive electrodes of both leads were placed on the left quality of the recorded ECG signal was compared (espe- side of the thorax behind the olecranon; the negative cially in relation to motion artefacts) to a signal recorded electrodes of both leads were positioned on the left side simultaneously with conventional Ag/AgCl electrodes. of the chest behind the withers; and the ground elec- The comparison showed that the capture system with trode above the left mid-thorax was placed between the textile electrodes was less prone to motion artefacts than remaining two pairs of electrodes. ECG tracing using conventional Ag/AgCl. The studies [19, 83, 84] presented a wearable ECG In addition to external monitors, one can also find monitoring system for the capture of cardiac activity in implanted loggers, which are defined as miniature, ani - horses. The system consisted of an electronic unit, a band mal-borne, electronic devices for logging and/or relaying K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 7 of 21 Measurement systems of data about an animal’s movement, behavior, physiol- This pilot study compares two methods of monitoring ogy and/or environment [98]. The advantage of these cardiac activity: the standard ECG-based system and an loggers is that they can also be used for measurements in alternative method based on ballistocardiography. The small animals, where the placement of external sensors details of the measurement systems are as follows: may be problematic or completely impossible, especially in animals moving in the air or water. In addition, as with external monitors, the sensor does not lose contact with 1. ECG measurement system—adhesive silver/silver the animal’s body. On the other hand, the risks associated chloride (Ag/AgCl) electrodes and a bioamplifier with the invasiveness of the method, such as inflamma - by g.tec medical engineering (Schiedlberg, Austria) tion or encapsulation at the site of implementation, must were used for ECG measurement. The g.USBamp be considered [98]. RESEARCH amplifier is a device of great accuracy One of the companies offering a wide range of differ - for measuring and processing biological signals ent types of implanted loggers is Star-Oddi hf (Garða- (the physiological activity of the eyes, brain, mus- bær, Iceland). Star-Oddi implanted loggers allowing cles, heart, and other organs). The amplifier is sup - to monitor HR derived from a leadless single channel plied with a USB interface and 16 simultaneous A/D ECG, temperature, real-time telemetry or the depth at delta-sigma type converters with 24-bit resolution which the animal is located. Star-Oddi implanted log- and sampling frequency range of 64 Hz–38.4 kHz. ger was used to assess ECG-derived HR in Atlantic The input range of this amplifier is ± 250 mV, which cod in [99], for evaluation of HR and swimming activ- allows direct voltage signals to be recorded without ity as stress indicators for Atlantic salmon in [100], saturation. The amplifier includes an internal unit for or for HR monitoring in large decapod crustaceans calibrating individual input channels and circuits for [101]. In mammals, the leggers were used for example measuring the impedance of individual electrodes. in [102], where the authors monitored cattles. Moreo- The block diagram of attachment for ECG measure - ver, in [103], the loggers were used in domestic sheep ment is shown in Fig. 3. All hardware and software monitoring. used is summarised in Table 4. 2. BCG measurement system—BCG measurement Materials and methods using a mechanical vibration sensor was performed This pilot study focuses on comparison of ECG and BCG simultaneously with ECG measurement. This meas - measurement systems based on sensing electrical and urement system was implemented with the following mechanical activity of the animal’s heart, respectively. devices by National Instruments (Austin, TX, USA): These systems have proved effective in measuring cardiac NI cDAQ-9185, which is a configurable chassis, and activity in humans. This section describes the equipment, the NI-9234 module. A microphone and sensor were attachment of the measurement systems, placement of also used to capture the mechanical vibrations pro- the ECG electrodes and BCG sensor, methods used to duced by the movement of blood inside large vessels. process the measured signals, and the parameters used The sensor was made from a spiral-shaped deform - to assess the accuracy of measurement. Measurement able plastic tube. was performed in four subjects: a goat, a cow, a horse, and a sheep. The subjects were provided by the Clinic of Movements of the animal’s body caused by quicker Ruminant and Pig Diseases and the Clinic of Horse Dis- blood flow vibrated the particles of the acoustically eases of the Veterinary and Pharmaceutical University in enclosed environment inside the spiral. Pressure changes Brno. A summary of the measured subjects is presented were transferred via a plastic tube to the measuring in Table 3. Table 3 Summary of measured subjects Animal Identification Number Breed Age (years) Note Expected HR values (BPM) Goat 09,088/968 White shorthaired goat 1 – 70–100 Cow 254,887/962 Holstein cow 3 Last delivery 21 days 60–80 ago (still-born calf ) Horse Private breeding Hanoverian horse 11 3 week post-abdomi- 30–40 nal surgery Sheep 092,807/961 Crossbreed 1 – 70–90 Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 8 of 21 gUSBamp PC Bandpassfilter Notch filter Sigma-delta 24bit 8th orderButtherworth 4th orderButtherworth LabVIEW ADC Right side Leftside fl =0.5 Hz,fh= 60 Hz fl =48Hz, fh =52Hz . . . RA GND + LA LL . . . REF . . . n= 16 n= 16 n= 16 Bandpassfilter Notch filter Sigma-delta 24bit Localdata 8th orderButtherworth 4th orderButtherworth ADC unit fl =0.5 Hz,fh= 60 Hz fl =48Hz, fh =52Hz Fig. 3 Block diagram for ECG measurement Table 4 Hardware and software used Name HW/SW Manufacturer Model Bioamplifier HW g.tec gUSBamp RESEARCH Configurable chassis HW National instruments cDAQ-9185 Measurement module HW National instruments NI-9234 Microphone HW GRAS 40PP CCP MATLAB SW MathWorks Matlab R2017a LabVIEW SW National instruments 2018 Device driver SW National instruments NI-DAQmx 19.5 Device driver SW National instruments NI-VISA 19.5 Device driver SW g.tec gUSBamp driver 3.16.00 g. Hlsys library SW g.tec 2.14.00 Electrode and bcg sensor placement microphone GRAS 40PP CCP by G.R.A.S. (Holte, Den- For the ECG measurements, the electrodes were posi- mark) and then converted into voltage signals. The tioned and attached according to the Einthoven triangle, microphone has a wide frequency range from 10 Hz to i.e., one electrode was placed on the right front leg, the 20 kHz and sensitivity of 50 mV/Pa. The signals were second on the left front leg, and the third electrode in then digitalised using the NI-9234 module, which is suit- the abdominal area to ensure that the animal’s heart was able for measuring sounds or vibrations from acceler- in the centre of the Einthoven triangle. To improve the ometers or microphones. The module has four channels, electrode adhesion and ECG signal quality, we sheared 24-bit resolution, a sampling frequency of 51.2 kS/s and the locations, where the electrodes were to be placed an input range of ± 5 V. The digitalised signal was sent via with electrical shears and the skin was cleaned with gel– the cDAQ-9185 ethernet chassis to a PC. This ethernet alcohol disinfectant solution septoderm from Schülke & interface is a four-slot compact data acquisition (DAQ) Mayr GmbH (Norderstedt, Germany). To reduce skin system designed for the collection of data or switching resistance and artifacts, these positions were also cleaned slow action members. The chassis has a controller with with abrasive fine sandpaper. The BCG sensor was placed configurable firmware responsible for timing, synchroni - on the left front leg to be as close to the animal’s heart as sation of measurement tasks and data transfer between possible while sufficiently adhering to the animal’s body the I/O modules and the external control unit. The block in view of its size. Details and examples of attachment in diagram of attachment for BCG measurement is shown the case of individual animals are summarised below. in Fig. 4. K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 9 of 21 Microphone GRAS 40PP CCP LabVIEW Leftside Chassis Module NI-9234 cDAQ-9185 Local data unit Fig. 4 Block diagram for BCG measurement The goat was cleaned and sheared thoroughly in loca - tions selected for attachment of the ECG electrodes Right side before commencing the measurement. The positions RA GND according to the Einthoven triangle were used to capture REF ECG signals. The BCG sensor was attached on the left front leg (see Fig. 5). Attachment of the ECG electrodes on the cow was a) b) also according to the Einthoven triangle. The cow was partially immobilised (enclosed in a cattle chute) for the Left side purposes of measurement, subsequently cleaned at the LA selected points and connected to the measurement sys- LL tem via electrodes. The BCG sensor was attached on the left front leg (see Fig. 6). The ECG measurement on the horse was per - formed according to a modified Einthoven triangle. Fig. 5 Example of (a) attachment diagram and (b) actual measurement of a goat Right side GND RA REF a) b) Left side LA LL Fig. 6 Example of (a) attachment diagram and (b) actual measurement of a cow Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 10 of 21 Right side GND REF RA a) b) Left side LA LL Fig. 7 Example of (a) attachment diagram and (b) actual measurement of a horse Right side RA GND REF a) b) Left side LA LL Fig. 8 Example of (a) attachment diagram and (b) actual measurement of a sheep The electrodes were relocated to the abdominal area. and carefully cleaned with gel–alcohol disinfectant The BCG sensor was attached to the left front leg (see solution. While the electrodes were placed according Fig. 7). to the general model, the exact positions of the elec- Finally, the attachment and placement of electrodes trodes are not evident in the image in Fig. 8b because for the ECG measurement system was the most compli- of the animal’s thick coat of wool. The BCG sensor was cated on the sheep due to the presence of lanolin on the attached to the left front leg (see Fig. 8). sheep’s skin, although the selected parts were sheared K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 11 of 21 Fig. 9 Comparison of the noisy and filtered signals with significant peaks highlighted: a noisy ECG signal from the LL lead in the goat and b is the ECG signal after application of the IIR Buttherworth filter, R peaks are marked red c noisy BCG signal from horse, and d the BCG signal filtered by IIR-WT, J peaks are marked red Signal processing ECG processing, was used to detect the J peak, which The first step in processing the ECG signals was filter - corresponds to the R peak in the ECG signal. The BCG ing. Poorly selected filters can affect the resulting sig - signals were also processed using MATLAB software. nals and their analysis significantly. The captured ECG An example of the originally measured (noisy) BCG signals were first filtered with the digital filters included signal from horse and the filtered BCG is provided in with the bioamplifier. MATLAB software by Math - Fig. 9c, d, respectively. Works (Natick, Massachusetts, USA) and the IIR But- terworth filter [104– 108] BPF type were then applied for subsequent processing of the measured signals. In Evaluation metrics our tests, the IIR Buttherworth filter was more effec - To verify the suitability of ECG for the purposes of tive in filtering interference than the FIR filter, which monitoring cardiac activity in animals and whether it altered the shape of the ECG signal, and may potentially can be used as a reference, we calculated and compared cause unnecessary loss of clinically significant informa - three very frequently used parameters and the aver- tion. The ECG signals for each animal and lead were age HR obtained from the individual leads. The stand - processed using a sixth order filter with cutoff frequen - ard deviation of the length of the NN interval (SDNN) cies set at 2 Hz and 40 Hz. This frequency band was is the simplest to calculate, as it is the square root of selected, since most of the ECG signal energy occupies the variance. The SDNN parameter may be inter - this frequency band. It is important to note that this is preted according to the statement that the higher the sufficient for the HR determination based on R peak SDNN parameter, the greater HRV, which also indi- detection, not for precise ECG morphological analy- cates increased adaptability of the autonomic nervous sis. An example of the originally measured ECG signal system. As the SDNN value decreases, the variability is from the LL lead on the goat and filtered signals is pro - less and only limited autonomic regulation is present. vided in Fig. 9a, b. Once the ECG signals were filtered, SDNN is expressed according to R peaks were detected using a detector which applied a continuous wavelet transform (Gaussian mother wave- SDNN = NN − NN , (1) let with a width of one and five levels of decomposition) N − 1 i=1 [109]. The distances between individual R peaks, i.e., RR intervals, were calculated and applied to ascertain where NN indicates the value of the ith NN interval, N the values of the current HR, the average HR, and the is the total number of intervals, and NN is the average HRV parameters. value of the NN intervals. Since the variance mathemati- The BCG signals were processed using a IIR Butter - cally equals the total power of spectral analysis, SDNN worth BPF type third order filter with cutoff frequen - reflects all the cyclic elements responsible for variability cies 5 Hz and 20 Hz in combination with a WT method in the period of recording [20]. In practice, it is not suit- [110–114] which applied the symlet8 wavelet and three able to compare the SDNN obtained from recordings of levels of decomposition. The same detector used for Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 12 of 21 different duration (because this quantity depends on the as a final step; the window was selected for each animal length of the recording period) [20]. (according to its HR) and ranged from 15 to 25 samples. The RMSSD parameter defined as the square root of Finally Bland–Altman plots were used to evaluate the the mean quadratic differences of consecutive NN inter - accuracy of the measured BCG signals in comparison vals can also be used. The RMSSD parameter is used to to the reference ECG signals. These plots are often used estimate the vagally mediated changes, which are also to compare two medical measurements. The average of reflected in HRV. When an animal is stressed, parasym - the measured pairs is recorded on the horizontal axis, pathetic activation is reduced and the RMSSD values are and the difference between these two measurements is thus lower. RMSSD is expressed according to recorded on the vertical axis. A 95% confidence interval is frequently applied to estimate the interval μ ± 1.96σ, where we can expect to find 95% of the difference values RMSSD = (NN − NN ) . (2) i+1 i [115]. Using Bland–Altman plots, the present study com- N − 1 i=1 pares the vectors and HR values obtained from the BCG signals to the HR values obtained from the ECG signals. The AVNN parameter is defined as the average NN interval duration. All these measurements of short-term Results variation estimate the high-frequency variation in the HR To obtain the best possible results, we tested the effect and are, therefore, heavily correlated [20, 37]. of the filtering on the measured signals and assessed The HR traces were also used to assess the precision of them visually. The best results were obtained using the ECG signals measured from individual leads. We also the IIR Buttherworth filter with cutoff frequencies of evaluated whether the values of the average HR matched 2 Hz and 40 Hz (i.e., the range of the useful ECG sig- the mental condition of the animals during the measure- nal). An example of the resulting filtered signals in all ment. To capture this, values of the current HR values the animals tested is given in Fig. 10. The LA lead is had to be derived first. This was achieved using a R peaks shown for the goat, and cow, and the LL lead is shown detector. The intervals between individual R peak posi - for the horse and sheep. The results in the figure show tions were derived and converted to the current HR val- that the signals were filtered suitably and that the ECG ues in BPM according to signal did not deteriorate because of an inappropriately selected filtering technique. Despite some of the ani - HR = · 60 (3) mals being calm during measurement, a small propor- tion of the signals were affected by motion artefacts. where T is the peak-to-peak time difference, and HR is Therefore, some of the R-peaks could not be detected the resulting heart rate. The moving average was applied a) e) b) Time (s) c) f) d) 260 261 262 263264 265 27 28 29 30 31 32 Time (s) Time (s) Fig. 10 Example of filtered clean ECG signals in each of the tested animals and ECG signals affected by motion artifacts: a filtered signal (LA lead, goat), b filtered signal (LA lead, cow), c filtered signal (LL lead, horse), d filtered signal (LL lead, sheep); e with motion artifacts (LA lead, goat); f with motion artifacts (LL lead, sheep) K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 13 of 21 Table 5 Values of the average HR and values of the HRV measured in individual animals determined from the ECG leads (LL, RA, and LA) and BCG signal Animal Channel Average HR (BPM) RMSSD (ms) SDNN (ms) AVNN (ms) Goat ECG-LL 122.87 533.70 152.40 511.47 ECG-RA 121.55 525.10 124.30 510.21 ECG-LA 121.76 531.90 138.40 513.57 BCG 126.46 808.05 208.31 494.52 Cow ECG-LL – – – – ECG-RA 57.89 1072.40 154.30 1061.20 ECG-LA 57.94 1075.60 168.10 1062.41 BCG 57.42 1221.43 203.78 1158.71 Horse ECG-LL 36.51 1650.50 98.00 1647.62 ECG-RA – – – – ECG-LA 36.56 1644.60 99.60 1643.58 BCG 36.66 1748.40 120.84 1707.30 Sheep ECG-LL 135.30 467.80 84.70 460.07 ECG-RA – – – – ECG-LA – – – – BCG – – – – shown in Fig. 10e, f. Example (e) represents part of the signal ECG signal in the goat from the LA lead, exam- a) ple (f ) shows the ECG signal of the sheep from the LL lead. Figure. 11 shows the examples of the BCG signals after filtration. We can see that the visual quality of the b) signals is lower than in case of ECG measurement. To compare the quality of those methods objectively, we used Bland–Altman analysis (Table 6 and Fig. 14) and investigated the differences between the obtained HRV c) traces (Figs. 12 and 13). Table 5 summarizes the obtained values of the aver- 96 97 98 99 100 101 age HR and values of the HRV measured in individual Time( s) animals. Higher values of average HR were expected Fig. 11 Examples of filtered BCG signals in each of the tested in the case of the goat and sheep, since both of these animals: a goat, b cow, c horse animals were stressed and trembling during measure- ment. Measurement in the cow and horse proceeded without problems, since the cow and horse were calm Table 6 Mean values d and values of ±1.96s measured from during measurement. The values of the average HR BCG signals were, therefore, expected to fall within the physiologi- Measurement system Animal ± 1.96 s cal range. (BPM) (BPM) The signals from all leads (LL, RA, LA) during ECG measurement in the goat were captured in high quality BCG Goat 3.63 51.56 and could, therefore, be used for further analysis along Cow − 0.53 6.91 with the BCG signal. As shown by results in Table 5, there Horse − 0.24 2.35 were similar average values of HR and the HRV param- eters obtained from individual ECG leads with a negligi- ble difference between individual leads in the evaluation and this caused minor differences between individual metrics. The average HR for the LL lead was 122.87 BPM, leads in the analysis (see Table 5). The effect of the the average HR for the RA lead was 121.55 BPM, and the animals’ movements on the quality of ECG capture is Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 14 of 21 BCG BCG BCG ECG ECG ECG a) b) c) 25 26 27 28 29 30 237238 239240 241242 383384 385386 387388 Time (s) Time (s) Time (s) BCG ECG 0 100 200300 400500 600 Time (s) BCG BCG BCG ECG ECG ECG d) e) f) 106 107 108109 110111 340341 342 343 344345 552553 554555 556557 Time (s) Time (s) Time (s) Fig. 12 Illustration of the effect of the quality of recording on the HR traces (horse). Examples a, b and c correspond to the sections, where high accuracy was achieved in determining the HR; examples d, e and f correspond to the sections, where determining the HR was less accurate highest HR for the RA lead was 122.87 BPM. In case of and 59.60 ms, respectively. In case of BCG channel, an BCG channel, an average HR of 126.46 BPM was calcu- average HR of 36.66 BPM was calculated from the BCG lated from the BCG signal in goat. signal. Only two ECG signals (RA, LA leads) measured in the Measurement of the sheep was the most difficult due to cow were used for further analysis along with the BCG significant restlessness in the animal. Only one captured signal, since the data measured from the third lead was signal (from the LL lead) was useful for further analysis. not of suitable quality and thus could not be used. An The average HR measured with the LL lead was 135.30 analysis was, therefore, performed on the data obtained BPM. from the leads on the right and left sides (RA and LA, To obtain accurate information about the HR, the sig- respectively). The values of the parameters obtained from nals must be of sufficient quality so that the significant the signals in both leads showed only slight deviation in peaks can be detected. This is illustrated in Figs. 12 and the evaluation metrics. The average HR was 57.89 BPM 13 that show examples of HR traces determined using in the RA lead and 57.94 BPM in the LA lead. An average both ECG and BCG signals in horse and cow. There are HR of 57.42 BPM was calculated from the BCG signal in parts, where the HR traces overlap (i.e., both methods the cow. determined the same HR) and also parts, where they dif- Signals from the LA and LL leads were also captured fer. In both figures, the examples (a), (b) and (c) corre - from the horse. The values of the HR and HRV param - spond to the sections, where determining the HR showed eters obtained from both leads were similar, with a neg- a high level of accuracy. These are well captured BCG sig - ligible difference in in evaluation metrics. The average nals. In contrast, examples (d), (e) and (f ) correspond to HR was 36.51 BPM in the LL lead and 36.56 BPM in the the sections, where the HR trace in BCG deviated from LA lead, while the SDNNs for these leads were 98.00 ms, HeartRate(bpm) K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 15 of 21 BCG BCG BCG ECG ECG ECG a) b) c) 94 95 96 97 98 99 281282 283284 285 286 594 595 596597 598599 Time (s) Time (s) Time (s) BCG ECG 0100 200300 400 500 600 Time (s) BCG BCG BCG ECG ECG ECG d)e)f) 235236 237 238239 240 490 491 492493 494 495 119120 121 122 123124 Time (s) Time (s) Time (s) Fig. 13 Illustration of the effect of the quality of recording on the resulting HR traces (cow). Examples a, b and c correspond to the sections, where high accuracy was achieved in determining the HR; examples d, e and f correspond to the section, where determining the HR was less accurate 120 120 80 80 40 40 0 0 0 -40 -40 -40 -80 -80 -80 90 110 130 150 170 190 30 35 40 50 60 70 80 (BCG+ ECG)/2 (bpm) (BCG+ ECG)/2(bpm) (BCG+ ECG)/2 (bpm) (a)(b) (c) Fig. 14 Comparison based on the Bland–Altman plots of the reference values and the estimated BCG values in HR measurements of a the goat, b the cow and c the horse. The middle horizontal line indicates the mean d of all differences. The upper and lower horizontal dashed lines indicate 95% limits of agreement that lie in the interval d ± 1.96 s (BCG -ECG)(bpm) HeartRate(bpm) (BCG -ECG)(bpm) (BCG -ECG)(bpm) Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 16 of 21 the reference ECG trace. This deviation resulted from 36.51 BPM and 36.56 BPM in the LL and LA ECG lead, interference which reduced the quality of the signal. respectively. This reflects the physiological values, which Finally, to objectively compare the quality of the sig- fall in the range of 30–40 BPM with an average value of nals acquired by the ECG and BCG methods, Bland– 35 BPM [117]. The values confirm the assumption that Altman analysis was used, see Table 6 and Fig. 14. We the animal was not stressed. can interpret the data in a way that the less the range of However, the situation in the goat and sheep was dif- the confidence interval, the less the difference between ferent, which is evident in HR values and also on the sig- the HR from the BCG signals and the HR from the ref- nal quality. The average HR in goat for the LL, LA, and erence ECG. The values should ideally be near the bias RA ECG lead was 122.87 BPM, 121.55 BPM, and 122.87 horizontal line, which should be close to zero, and BPM, respectively. These values were slightly higher than show a mean d for all differences. The mean values d the physiological values, which were expected within the and values of ±1.96s are summarised in Table 6. The range of 70–100 BPM with an average value of 90 BPM BCG signals could only be captured in the goat, cow, [116]. The average HR values, therefore, confirm the and horse, while the attempt in the sheep was not suc- hypothesis that the goat was stressed, and the ECG meas- cessful. According to the results presented in Table 6 urement can be considered precise. These results (high and because high values of d and ±1.96s were deter- HR values) are also consistent with our observations that mined, we can state that the BCG method was not the goat appeared unsettled during HR recordings. effective in the goat. The BCG method in the cow and The sheep was the most problematic subject in ECG horse was effective, because low values of d and values measurement mainly because of the animal’s thick coat of ±1.96s were achieved in each case. Figure 14a shows of wool and lanolin layer on the skin. In this case, the the Bland–Altman plots for signals captured in the electrodes could not be attached securely on the animal’s goat, where measurement with BCG failed entirely, and body despite shaving and addressing the skin with alco- examples (b) and (c) present the signals measured in hol solution. Only one of the ECG signals was, therefore, cow and horse, respectively, where the measurements used for analysis. The average HR measured with the LL were effective. lead was 135.30 BPM. These is an extremely high value compared to the physiological values, which fall in the Discussion range of 70–90 BPM [116]. It is, therefore, evident, simi- The present study examined the usability of alternative larly as in the case of the results in goat, that the animal measurement systems in monitoring the cardiac activ- was highly stressed and unsettled during the measure- ity of animals. The accuracy of the ballistocardiography ment procedure. method was compared to the verified standard, i.e., the The problem regarding the measurements was that the ECG measurement system. Herein, this assumption animals were not used to this (or any) kind of monitor- was verified for the used ECG measurement system by ing and thus the preparations, especially in case of ECG, comparing the values obtained from individual leads were stressful for them. The ECG measurement included (Table 5). Measurement of the cardiac activity in animals shaving in several parts of their body, application of alco- using ECG was considered representative and was, thus, hol to clear these areas and also addressing the conduc- used as a reference or ground truth. During the meas- tivity between the electrode and the skin by means of urements, we encountered several problems and were sandpaper. This procedure of shaving fur, attaching the unable to satisfactorily measure all the signals in some electrodes and measuring ECG was particularly demand- animals. This section discusses the possible reasons for ing in goat and sheep, which was also reflected in the these problems and offers practical insights into each measurements (higher HR values, motion artefacts). method. Therefore, the future research should include compari - Measurement in the cow and horse proceeded without son of the measurements when it is used along with the problems, since the cow and horse were not unsettled ECG and alone. We believe that when the BCG sensor during measurement. The values of the average HR were, is incorporated in a saddle or a belt and thus placed on therefore, expected to fall within the physiological range. the animal quickly and easily, the HR will be lower (i.e., As for the measurement in cow, the average HR was 57.89 the animal will be less stressed) than when the process BPM and 57.94 BPM in the RA and LA lead, respectively, includes preparations for the ECG based monitoring. indicating slightly lower values than the physiological val- One of the advantages of the BCG sensor was quick ues in cows, which fall in the range of 60–80 BPM with an and simple attachment to the animal without the need average value of 70 BPM [116]. The results confirmed the for shaving. As this is an alternative method for the hypothesis that the cow was calm during measurement. measurement of cardiac activity, no standard guidelines In case of the measurement in horse, the average HR was or recommendations are available for sensor placement. K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 17 of 21 The BCG sensor was placed on the left front leg to be as the useful signal. These adaptive algorithms have proved close as possible to the animal’s heart and for the best useful in the past in processing human biological signals, adherence to the animal’s body according to its size. such as BCG and ECG in adults [118–120], fetal ECG However, the size of the sensor contributed to poor adhe- [121–123], speech signals [124, 125], or signals used in sion to the body of the animal and motion artefacts in telecommunications [126]. the captured signal due to movement of the sensor with The pilot study introduced research in the given area, small animals. During measurement of BCG signal in the and future research will focus on measuring the car- goat, the sensor moved frequently because of the animal’s diac activity in cattle and horses. Measurement of BCG restlessness, resulting in motion artefacts and significant in these animals is shown to be precise, and the benefits distortions to the signal. A high-quality BCG signal could may be economically significant (e.g., in milk produc - not be obtained from the goat even after filtering, and tion or racehorse training). In future, additional sensor the detector was unable to detect the J peaks correctly. types and measurement methods to obtain HR at rest The BCG signal could not be captured at all in the sheep or in motion will be tested. This has an important con - because of the wool covering the animal’s body. tribution, for example, in horse training. Advanced fil - However, sufficient contact between the sensors and tering methods (adaptive algorithms) will also be tested the animal’s body was achieved with the larger ani- to measure the cardiac activity of animals under load. In mals, i.e., the cow and horse. The BCG signals were only the case of horses, measurement of HR recovery, which is affected slightly by motion artefacts, and high-quality defined as the reduction in the HR 1 min after training, is signals were captured. This was confirmed by the results a major indicator of the horse’s form. obtained from the Bland–Altman plots, where in horse Future research will also focus on determining a gold and cow, the results showed agreement between the two standard for the placement of sensors and electrodes in methods. On the other hand, in case of the poor BCG individual animal species and thereby facilitate the best measurements in goat, the results of Bland–Altman possible quality in future records. This could also lead to analysis show lack of agreement with the ECG reference. the creation of a measurement system for use in small The findings are also reflected in the HR analysis of the farms up to large breeding operations to monitor the animals on both data, especially on the average HRs. The health and well-being of animals. Creating optimal con- average of 36.66 BPM was calculated from the BCG sig- ditions for the life of animals or the early detection of nal in the horse, which matched the average HR deter- disease may help reduce economic loss and increase pro- mined from the ECG signals (36.51 and 36.56 BPM). An ductivity in farms. The use of HRV monitoring during the average HR of 57.42 BPM calculated from the BCG sig- training of race animals is a separate topic which is cur- nal in the cow matched the average HR determined from rently enjoying attention. This method may help optimise the ECG signals (57.89 BPM and 57.94 BPM). Contrary, the training, recuperation and physical readiness of ani- in case of the goat, the obtained HR average was 126.46 mals and thus improve their form and performance. BPM, which is higher than the averages of the HRs obtained using the ECG leads (122.87 BPM, 121.55 BPM, Conclusions and 121.76 BPM). The present study focused on the measurement of car - Obtaining a high-quality signal to monitor the car- diac activity in animals using ECG and BCG systems in diac activity of animals by means of BCG method thus four animals: a goat, a cow, a horse, and a sheep. First, depends on the selection of suitable sensor size (corre- the suitability of ECG was verified by comparing the cal - sponding to the animal’s size), placement of sensors and culated evaluation parameters (the average HR, RMSSD, sufficient contact with the animal’s skin. We, therefore, SDNN and AVNN) and comparing the HR traces from recommend integrating sensors into a sensor belt or individual leads. The relation between the average HR a saddle (for horses) in future research. This monitor - values and the observed mental state and behaviour of ing method may provide sufficient contact to minimise the animals during measurement was also discussed. the movement of the sensors. In addition to the sensors Because ECG was considered a valid baseline for compar- which capture cardiac activity, a reference sensor can be ison, it was used in this study as a reference for assessing placed elsewhere on the animal to capture the signals the accuracy of the BCG method. The captured signals with interference from motion, while the animal is mov- were analysed by comparing the accuracy of the current ing (in particular horses under load). The signal which HR values using Bland–Altman plots. Measurements includes the horse’s cardiac activity and motion artefacts of the BCG signals were accurate in the goat, cow, and and the signal which contains only motion signals from horse. BCG signals could not be measured in the sheep, the reference sensor could be used as inputs for an adap- since BCG sensor was highly prone to poor adhesion to tive algorithm which can suppress the interference in the animal’s body and movement, while the animal was Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 18 of 21 Competing interests restless, which resulted in motion artefacts. The BCG The authors declare that they have no competing interests. sensor was only shown to be effective with the large ani - mals, i.e., the horse and cow, where sufficient contact Author details Department of Cybernetics and Biomedical Engineering, VSB—Technical Uni- between the sensor and the animal’s body was achieved versity of Ostrava, Ostrava, Czechia. Faculty of Veterinary Medicine, Ruminant owing to their size. and Swine Clinic, University of Veterinary Sciences Brno, Brno, Czechia. In the future research, the placement of sensors should Received: 2 August 2021 Accepted: 3 April 2022 be optimised for individual species to allow capture of the highest possible quality of signal. The measurement system could be embedded into a tailor-made saddle or belt so that the contact of the sensor is ensured, and the References time required for the attachment is minimized. Further 1. Buchanan JW. The history of veterinary cardiology. J Vet Cardiol. tests should also focus on testing additional sensor types 2013;15(1):65–85. https:// doi. org/ 10. 1016/j. jvc. 2012. 12. 002. 2. Detweiler DK. Comparative cardiology and cardiovascular disease. J Clin and measurements with animals which are at rest but Epidemiol. 1962;15(9):867–78. https:// doi. org/ 10. 1016/ 0021- 9681(62) also in motion. Finally, advanced signal processing meth- 90056-5. ods will play a crucial role to eliminate the motion arti- 3. Detweiler DK, Patterson DF. The prevalence and types of cardiovascular disease in dogs. Ann N Y Acad Sci. 1965;127(1):481–516. https:// doi. org/ facts more efficiently. 10. 1111/j. 1749- 6632. 1965. tb494 21.x. 4. Detweiler DK. Generic aspects of cardiovascular diseases in animals. Circulation. 1964;30:114–27. https:// doi. org/ 10. 1161/ 01. cir. 30.1. 114. Abbreviations 5. Patterson DF, Detweiler DK, Glendenning SA. Heart sounds and HR: Heart rate; HR : Maximum heart rate; HR : Minimum heart rate; HRV: max min murmurs of the normal horse*. Ann N Y Acad Sci. 1965;127(1):242–305. Heart rate variability; ECG: Electrocardiography; BCG: Ballistocardiography; https:// doi. org/ 10. 1111/j. 1749- 6632. 1965. tb494 07.x. PCG: Phonocardiography; PPG: Photoplethysmography; RMSSD: Root mean 6. Detweiler DK. The Mammalian Electrocardiogram: Comparative Fea- square of successive RR interval differences; SDNN: Standard deviation of tures. In: Macfarlane PW, van Oosterom A, Pahlm O, Kligfield P, Janse M, normal-to-normal interval duration; AVNN: Average normal-to-normal interval Camm J, editors. Comprehensive Electrocardiology. London: Springer; duration; SDANN: Standard deviation of the average normal-to-normal inter- 2010. https:// doi. org/ 10. 1007/ 978-1- 84882- 046-3_ 42. vals; pNN50: Percentage of successive NN intervals; SEM: Standard error of the 7. Detweiler DK, Luginbuhl H, Buchanan JW, Patterson DF. The natural mean NN interval; LL, RA, LA: Left leg, right arm, left arm ECG leads; IIR: Infinite history of acquired cardiac disability of the dog*. Ann N Y Acad Sci. impulse response; FIR: Finite impulse response; WT: Wavelet transform.; DAQ: 1968;147(8):318–29. https:// doi. org/ 10. 1111/j. 1749- 6632. 1968. tb455 Compact data acquisition. 65.x. 8. Manuja B, Manuja A, Aich P. Stress and its impact on farm animals. Front Acknowledgements Biosci. 2012;E4:1759–67. Not applicable. 9. Gebregeziabhea E. The effect of stress on productivity of animals: a review. J Biol Agric. 2015;5(3):165. Author contributions 10. Carvalho ER, Franco GG. Heart rate variability as a non-invasive method RM created the concept and supervised the project. RK designed the concept to assess welfare in dairy cows. Approaches in poultry. Behav Sci. for the measurement setup, sensor placement, and signal processing. JB 2018;3(1):1–3. https:// doi. org/ 10. 31031/ APDV. 2018. 03. 000553. created the new software tool for measurement and analysis of the animal 11. Bennet L, Gunn AJ. The fetal heart rate response to hypoxia: insights data. JK designed and implemented the hardware tools of the alternative from animal models. Clin Perinatol. 2009;36(3):655–72. https:// doi. org/ pneumatic sensors. RK, JB, JK, and IS carried out the data acquisition. RK, JB, 10. 1016/j. clp. 2009. 06. 009. and KB analysed and interpreted the cardiac data. KB, RK, IS, and JK contrib- 12. Turbill C, Ruf T, Mang T, Arnold W. Regulation of heart rate and rumen uted in writing and revising the manuscript. All authors read and approved temperature in red deer: effects of season and food intake. J Exp Biol. the final manuscript. 2011;214(6):963–70. https:// doi. org/ 10. 1242/ jeb. 052282. 13. Hopster H, Blokhuis HJ. Validation of a heart-rate monitor for measuring Funding a stress response in dairy cows. Can J Anim Sci. 1994;74(3):465–74. This work was supported in part by the Ministry of Education of the Czech https:// doi. org/ 10. 4141/ cjas94- 066. Republic under Project SP2021/32, in part by the European Regional Develop- 14. Caroprese M, Albenzio M, Marzano A, Schena L, Annicchiarico G, Sevi ment Fund in the Research Centre of Advanced Mechatronic Systems Project A. Relationship between cortisol response to stress and behavior, within the Operational Programme Research, Development and Education immune profile, and production performance of dairy ewes. J Dairy Sci. under Project CZ.02.1.01/0.0/0.0/16-019/0000867. 2010;93(6):2395–403. https:// doi. org/ 10. 3168/ jds. 2009- 2604. 15. Rushen, J, de Passille AM, von Keyserlingk MAG, Weary DM. Stress and Availability of data and materials physiological indicators of animal welfare. In: Welfare H. Animal Welfare. The data sets used and/or analysed during the current study are available Dordrecht: Springer Netherlands; 2008. p. 43–69. https:// doi. org/ 10. from the corresponding author on reasonable request. 1007/ 978-1- 4020- 6558- 33. 16. Boissy A, Le Neindre P. Behavioral, cardiac and cortisol responses to brief peer separation and reunion in cattle. Physiol Behav. 1997;61(5):693–9. Declarations https:// doi. org/ 10. 1016/ s0031- 9384(96) 00521-5. 17. Kovács L, Jurkovich V, Bakony M, Szenci O, Póti P, Tozser J. Welfare Ethics approval and consent to participate implication of measuring heart rate and heart rate variability in dairy The data were collected in clinical conditions as part of project VFU-9-2019 cattle: literature review and conclusions for future research. Animal. approved by the competent University Bioethics Committee at the Faculty of 2014;8(2):316–30. https:// doi. org/ 10. 1017/ S1751 73111 30021 40. Veterinary Medicine, Ruminant and Swine Clinic, University of Veterinary and 18. Kuwahara M, Hashimoto S, Ishii K, Yagi Y, Hada T, Hiraga A, et al. Assess- Pharmaceutical Sciences Brno. ment of autonomic nervous function by power spectral analysis of heart rate variability in the horse. Auton Neurosci. 1996;60(1–2):43–8. Consent for publication https:// doi. org/ 10. 1016/ 0165- 1838(96) 00028-8. Not applicable. K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 19 of 21 19. Guidi A, Lanata A, Baragli P, Valenza G, Scilingo EP. A wearable system 40. Perkins JD, Bowen IM, Else RW, Marr CM, Mayhew IG. Functional and for the evaluation of the human horse interaction: a preliminary study. histopathological evidence of cardiac parasympathetic dysautonomia Electronics. 2016;5(4):63. https:// doi. org/ 10. 3390/ elect ronic s5040 063. in equine grass sickness. Vet Rec. 2000;146(9):246–50. https:// doi. org/ 20. Malik M. Heart rate variability. Ann Noninvasive Electrocardiol. 10. 1136/ vr. 146.9. 246. 1996;1(2):151–81. https:// doi. org/ 10. 1111/j. 1542- 474X. 1996. tb002 75.x. 41. Rietmann TR, Stauffacher M, Bernasconi P, Auer JA, Weishaupt MA. The 21. Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and association between heart rate, heart rate variability, endocrine and norms. Front Public Health. 2017;5:258. behavioural pain measures in horses suffering from laminitis. J Vet Med 22. Von Borell E, Langbein J, Després G, Hansen S, Leterrier C, Marchant- A Physiol Pathol Clin Med. 2004;51(5):218–25. https:// doi. org/ 10. 1111/j. Forde J, et al. Heart rate variability as a measure of autonomic 1439- 0442. 2004. 00627.x. regulation of cardiac activity for assessing stress and welfare in farm 42. Kuwahara M, Hiraga A, Nishimura T, Tsubone H, Sugano S. Power spec- animals—A review. Physiol Behav. 2007;92(3):293–316. https:// doi. org/ tral analysis of heart rate variability in a horse with atrial fibrillation. J Vet 10. 1016/j. physb eh. 2007. 01. 007. Med Sci. 1998;60(1):111–4. https:// doi. org/ 10. 1292/ jvms. 60. 111. 23. Yao Y, Shin S, Mousavi A, Kim CS, Xu L, Mukkamala R, et al. Unobtrusive 43. McConachie EL, Giguere S, Rapoport G, Barton MH. Heart rate variability estimation of cardiovascular parameters with limb ballistocardiography. in horses with acute gastrointestinal disease requiring exploratory Sensors. 2019;19(13):2922. https:// doi. org/ 10. 3390/ s1913 2922. laparotomy. J Vet Emerg Crit Care. 2016;26(2):269–80. https:// doi. org/ 24. Rushen J, Munksgaard L, Marnet PG, DePassillé AM. Human contact 10. 1111/ vec. 12362. and the effects of acute stress on cows at milking. Appl Anim Behav Sci. 44. Mésangeau D, Laude D, Elghozi JL. Early detection of cardiovascular 2001;73(1):1–14. https:// doi. org/ 10. 1016/ s0168- 1591(01) 00105-8. autonomic neuropathy in diabetic pigs using blood pressure and heart 25. Rushen J, De Passillé AM, Munksgaard L. Fear of people by cows and rate variability. Cardiovasc Res. 2000;45(4):889–99. https:// doi. org/ 10. effects on milk yield, behavior, and heart rate at milking. J Dairy Sci. 1016/ s0008- 6363(99) 00406-x. 1999;82(4):720–7. https:// doi. org/ 10. 3168/ jds. S0022- 0302(99) 75289-6. 45. Voss LJ, Bolton DPG, Galland BC, Taylor BJ. Endotoxin effects on markers 26. Joksimovic-Todorovic M, Davidovic V, Hristov S, Stankovic B. Eec ff t of of autonomic nervous system function in the piglet: implications for heat stress on milk production in dairy cows. Biotechnol Anim Husb. SIDS. Neonatology. 2004;86(1):39–47. https:// doi. org/ 10. 1159/ 00007 2011;27(3):1017–23. https:// doi. org/ 10. 2298/ BAH11 03017J. 7452. 27. Gygax L, Neuffer I, Kaufmann C, Hauser R, Wechsler B. Restlessness 46. Khan MS, Zhao N, Sica AL, Gootman N, Gootman PM. Changes in R-R behaviour, heart rate and heart-rate variability of dairy cows milked and Q-T intervals following cardiac vagotomy in neonatal swine. Exp in two types of automatic milking systems and auto-tandem milking Biol Med. 2001;226(1):32–6. https:// doi. org/ 10. 1177/ 15353 70201 22600 parlours. Appl Anim Behav Sci. 2008;109(2):167–79. https:// doi. org/ 10. 105. 1016/j. appla nim. 2007. 03. 010. 47. Janczarek I, Kedzierski W, Stachurska A, Wilk I, Kolstrung R, Strzelec K. 28. Chamove AS, Crawley-Hartrick OJE, Stafford KJ. Horse reactions to Autonomic nervous system activity in purebred Arabian horses evalu- human attitudes and behavior. Anthrozoos. 2002;15(4):323–31. https:// ated according to the low frequency and high frequency spectrum doi. org/ 10. 2752/ 08927 93027 86992 423. versus racing performance. Acta Vet Brno. 2016;85(4):355–62. https:// 29. Lesimple C, Fureix C, Menguy H, Hausberger M. Human direct actions doi. org/ 10. 2754/ avb20 16850 40355. may alter animal welfare, a study on horses (Equus caballus). PLoS ONE. 48. Voss B, Mohr E, Krzywanek H. Eec ff ts of aqua-treadmill exercise on 2010;5(4): e10257. https:// doi. org/ 10. 1371/ journ al. pone. 00102 57. selected blood parameters and on heart-rate variability of horses. J Vet 30. von Konig Borstel U, Euent S, Graf P, Konig S, Gauly M. Equine behaviour Med A Physiol Pathol Clin Med. 2002;49(3):137–43. https:// doi. org/ 10. and heart rate in temperament tests with or without rider or handler. 1046/j. 1439- 0442. 2002. 00420.x. Physiol Behav. 2011;104(3):454–63. https:// doi. org/ 10. 1016/j. physb eh. 49. Munsters CCBM, Visser EK, van den Broek J, van Sloet Oldruitenborgh- 2011. 05. 010. Oosterbaan MM. Physiological and behavioral responses of horses 31. Bartolomé E, Cockram MS. Potential effects of stress on the perfor - during police training. Animal. 2013;7(5):822–7. https:// doi. org/ 10. 1017/ mance of sport horses. J Equine Vet Sci. 2016;40:84–93. https:// doi. org/ S1751 73111 20023 27. 10. 1016/j. jevs. 2016. 01. 016. 50. Deprés G, Veissier I, Boissy A. Eec ff t of autonomic blockers on heart 32. Vetter S, Vasa L, Ózsvári L. Economic aspects of animal welfare. Acta period variability in calves: evaluation of the sympathovagal balance. Polytech Hung. 2014;11(7):119–34. Physiol Res. 2002;51(4):347–53. 33. McBride SD, Mills DS. Psychological factors affecting equine per - 51. Parker M, Goodwin D, Eager RA, Redhead ES, Marlin DJ. Comparison formance. BMC Vet Res. 2012;8(1):180. https:// doi. org/ 10. 1186/ of Polar heart rate interval data with simultaneously recorded ECG 1746- 6148-8- 180. signals in horses. Comp Exerc Physiol. 2009;6(4):137–42. https:// doi. org/ 34. Henningsen A, Czekaj TG, Forkman B, Lund M, Nielsen AS. The 10. 1017/ S1755 25401 00000 24. relationship between animal welfare and economic performance at 52. Behar JA, Rosenberg AA, Weiser-Bitoun I, Shemla O, Alexandrovich A, farm level: a quantitative study of Danish pig producers. J Agric Econ. Konyukhov E, et al. PhysioZoo: a novel open access platform for heart 2018;69(1):142–62. https:// doi. org/ 10. 1111/ 1477- 9552. 12228. rate variability analysis of mammalian electrocardiographic data. Front 35. Sinclair M, Fryer C, Phillips CJC. The benefits of improving animal wel- Physiol. 2018. https:// doi. org/ 10. 3389/ fphys. 2018. 01390. fare from the perspective of livestock stakeholders across Asia. Animals. 53. Vörös K, Nolte I, Hungerbuhler S, Reiczigel J, Ehlers JP, Tater G, et al. 2019;9(4):123. https:// doi. org/ 10. 3390/ ani90 40123. Sound recording and digital phonocardiography of cardiac murmurs in 36. Minero M, Canali E. Welfare issues of horses: an overview and practical dogs by using a sensor-based electronic stethoscope. Acta Vet Hung. recommendations. Ital J Anim Sci. 2009;8(sup1):219–30. https:// doi. org/ 2011;59(1):23–35. https:// doi. org/ 10. 1556/ AVet. 59. 2011.1.3. 10. 4081/ ijas. 2009. s1. 219. 54. Brown CM, Holmes JR. Phonocardiography in the horse: 1. The intracar- 37. Kovács L, Kézér FL, Jurkovich V, Kulcsár-Huszenicza M, Tőzsér J. Heart diac phonocardiogram. Equine Vet J. 1979;11(1):11–8. https:// doi. org/ rate variability as an indicator of chronic stress caused by lameness in 10. 1111/j. 2042- 3306. 1979. tb012 85.x. dairy cows. PLoS ONE. 2015;10(8): e0134792. https:// doi. org/ 10. 1371/ 55. Brown CM, Holmes JR. Phonocardiography in the horse: 2. The relation- journ al. pone. 01347 92. ship of the external phonocardiogram to intracardiac pressure and 38. Stubsjøen SM, Knappe-Poindecker M, Langbein J, Fjeldaas T, Bohlin J. sound. Equine Vet J. 1979;11(3):183–6. https:// doi. org/ 10. 1111/j. 2042- Assessment of chronic stress in sheep (part II): exploring heart rate vari-3306. 1979. tb013 37.x. ability as a non-invasive measure to evaluate cardiac regulation. Small 56. Gargiulo GD, Shephard RW, Tapson J, McEwan AL, Bifulco P, Cesarelli Rumin Res. 2015;133:30–5. https:// doi. org/ 10. 1016/j. small rumres. 2015. M, et al. Pregnancy detection and monitoring in cattle via combined 10. 026. foetus electrocardiogram and phonocardiogram signal processing. 39. Nagel C, Melchert M, Aurich C, Aurich J. Differences in endocrine and BMC Vet Res. 2012;8(1):164. https:// doi. org/ 10. 1186/ 1746- 6148-8- 164. cardiac changes in mares and her fetus before, during, and after parturi- 57. Wijshoff R, Sar TVd, Aarts R, Woerlee P, Noordergraaf G. Potential of tion in horses of different size. Animals. 2020;10(9):1577. https:// doi. org/ photoplethysmography to guide pulse checks during cardiopulmonary 10. 3390/ ani10 091577. Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 20 of 21 resuscitation: observations in an animal study. Resuscitation. 2013;84:1. 77. Heise D, Skubic M. Monitoring pulse and respiration with a non-invasive https:// doi. org/ 10. 1016/j. resus citat ion. 2013. 08. 019. hydraulic bed sensor. In: 2010 Annu Int Conf IEEE Eng Med Biol Soc; 58. Borchevkin D. Method of photoplethysmography diagnostics of 2010. p. 2119–23. ISSN: 1558–4615. https:// doi. org/ 10. 1109/ IEMBS. 2010. domesticated animals cardiovascular diseases. J Ve Sci Technol. 2015. 56272 19. https:// doi. org/ 10. 4172/ 2157- 7579. 10002 87. 78. Lydon K, Su BY, Rosales L, Enayati M, Ho KC, Rantz M, et al. Robust heart- 59. Cugmas B, Struc E, Spigulis J. Photoplethysmography in dogs and cats: beat detection from inhome ballistocardiogram signals of older adults a selection of alternative measurement sites for a pet monitor. Physiol using a bed sensor. In: 2015 Annu Int Conf IEEE Eng Med Biol Soc; 2015. Meas. 2019;40(1):01NT02. https:// doi. org/ 10. 1088/ 1361- 6579/ aaf433. p. 7175–9. ISSN: 1558–4615. https:// doi. org/ 10. 1109/ EMBC. 2015. 73200 60. Nie L, Berckmans D, Wang C, Li B. Is continuous heart rate monitoring 47. of livestock a dream or is it realistic? A review. Sensors. 2020;20(8):2291. 79. Chen Z, Teo JT, Ng SH, Yang X. Portable fiber optic ballistocardiogram https:// doi. org/ 10. 3390/ s2008 2291. sensor for home use. In: Proceedings Volume 8218, Optical Fibers 61. Cui Y, Zhang M, Li J, Luo H, Zhang X, Fu Z. WSMS: wearable stress and Sensors for Medical Diagnostics and Treatment Applications monitoring system based on IoT multi-sensor platform for living sheep XII; 82180X, San Francisco, California, United States. pp. 82180X-1– transportation. Electronics. 2019;8(4):441. https:// doi. org/ 10. 3390/ elect 82180X-7. https:// doi. org/ 10. 1117/ 12. 909768. ronic s8040 441. 80. Deepu CJ, Chen Z, Teo JT, Ng SH, Yang X, Lian Y. A smart cushion for 62. Theodor M, Ruh D, Subramanian S, Förster K, Heilmann C, Beyersdorf F, real-time heart rate monitoring. In: IEEE Trans Biomed Circuits Syst; Plachta D, Manoli Y, Zappe H, Seifert A. Implantable pulse oximetry on 2012. p. 53–6. ISSN: 2163–4025. https:// doi. org/ 10. 1109/ BioCAS. 2012. subcutaneous tissue. In: 2014 36th annual international conference of 64185 12. the ieee engineering in medicine and biology society. pp. 2089–2092; 81. Fung P, Dumont G, Ries C, Mott C, Ansermino M. Continuous noninva- 2014. sive blood pressure measurement by pulse transit time. In: 2004 Annu 63. Pretorius PJ, Terblanche M, van der Walt JJ. Ballistocardiography in some Int Conf IEEE Eng Med Biol Soc. vol. 1; 2004. p. 738–41. https:// doi. org/ domestic animals. Bibl Cardiol. 1968;21:46–52.10. 1109/ IEMBS. 2004. 14032 64 64. Cossio P, Berreta JA, Mosso HE, Perianes I. Ballistocardiogram in experi- 82. Lenoir A, Trachsel DS, Younes M, Barrey E, Robert C. Agreement mental myocardial lesions. Cardiology. 1954;24(6):372–7. https:// doi. between electrocardiogram and heart rate meter is low for the meas- org/ 10. 1159/ 00016 5432. urement of heart rate variability during exercise in young endurance 65. Chakrabarti T, Saha S, Roy S, Chel I. Phonocardiogram signal analysis— horses. Front Vet Sci. 2017. https:// doi. org/ 10. 3389/ fvets. 2017. 00170. practices, trends and challenges: a critical review. 2015 International 83. Lanata A, Guidi A, Baragli P, Valenza G, Scilingo EP. A novel algorithm Conference and Workshop on Computing and Communication (IEM- for movement artifact removal in ECG signals acquired from wearable CON). 2015. https:// doi. org/ 10. 1109/ IEMCON. 2015. 73444 26. systems applied to horses. PLoS ONE. 2015;10(10): e0140783. https:// 66. Anumukonda M, Chowdhury SR. Heart sound sensing through MEMS doi. org/ 10. 1371/ journ al. pone. 01407 83. microphone. In: Postolache OA, Mukhopadhyay SC, Jayasundera KP, 84. Lanata A, Guidi A, Baragli P, Paradiso R, Valenza G, Scilingo EP. Removing Swain AK, editors. Sensors for Everyday Life: Healthcare Settings. Smart movement artifacts from equine ECG recordings acquired with textile Sensors Measurement and Instrumentation. Cham: Springer; 2017. p. electrodes. In: 2015 Annu Int Conf IEEE Eng Med Biol Soc 2015. p. 121–34. https:// doi. org/ 10. 1007/ 978-3- 319- 47319- 27. 1955–8. ISSN: 1558–4615. https:// doi. org/ 10. 1109/ EMBC. 2015. 73187 67. 67. Leng S, Tan RS, Chai KTC, Wang C, Ghista D, Zhong L. The electronic 85. Stewart M, Verkerk GA, Stafford KJ, Schaefer AL, Webster JR. Noninvasive stethoscope. Biomed Eng Online. 2015;14(1):66. https:// doi. org/ 10. assessment of autonomic activity for evaluation of pain in calves, using 1186/ s12938- 015- 0056-y.4. surgical castration as a model. J Dairy Sci. 2010;93(8):3602–9. https:// 68. Goovaerts HG, van Geijn HP, Rompelman O. An inductive sensor for doi. org/ 10. 3168/ jds. 2010- 3114. recording of fetal movements and sounds. In: 1991 Annu Int Conf IEEE 86. Stewart M, Shepherd HM, Webster JR, Waas JR, McLeay LM, Schutz KE. Eng Med Biol Soc; 1991. p. 1622–3. https:// doi. org/ 10. 1109/ IEMBS. 1991. Eec ff t of previous handling experiences on responses of dairy calves to 684669. routine husbandry procedures. Animal. 2013;7(5):828–33. https:// doi. 69. Martinek R, Nedoma J, Fajkus M, Kahankova R, Konecny J, Janku P, et al. org/ 10. 1017/ S1751 73111 20022 5X. A phonocardiographic-based fiber-optic sensor and adaptive filtering 87. Hagen K, Langbein J, Schmied C, Lexer D, Waiblinger S. Heart rate vari- system for noninvasive continuous fetal heart rate monitoring. Sensors. ability in dairy cows—influences of breed and milking system. Physiol 2017;17(4):890. https:// doi. org/ 10. 3390/ s1704 0890. Behav. 2005;85(2):195–204. https:// doi. org/ 10. 1016/j. physb eh. 2005. 03. 70. Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable 019. photoplethysmography sensors and their potential future applica- 88. Zucca E, Ferrucci F, Di Fabio V, Croci C, Ferro E. The use of electrocar- tions in health care. Int j biosens. 2018;4(4):195–202. https:// doi. org/ 10. diographic recording with Holter monitoring during treadmill exercise 15406/ ijbsbe. 2018. 04. 00125. to evaluate cardiac arrhythmias in racehorses. Vet Res Commun. 71. Kamal AAR, Harness JB, Irving G, Mearns AJ. Skin photoplethysmogra- 2003;27(Suppl 1):811–4. https:// doi. org/ 10. 1023/B: VERC. 00000 14278. phy—a review. Comput Methods Programs Biomed. 1989;28(4):257–69. 44422. 9d. https:// doi. org/ 10. 1016/ 0169- 2607(89) 90159-4. 89. Langbein J, Nürnberg G, Manteuffel G. Visual discrimination learning in 72. Giovangrandi L, Inan OT, Wiard RM, Etemadi M, Kovacs GTA. Ballisto- dwarf goats and associated changes in heart rate and heart rate vari- cardiography—A method worth revisiting. In: 2011 Annu Int Conf IEEE ability. Physiol Behav. 2004;82(4):601–9. https:// doi. org/ 10. 1016/j. physb Eng Med Biol Soc; 2011. p. 4279–82. ISSN: 1558–4615. https:// doi. org/ eh. 2004. 05. 007. 10. 1109/ IEMBS. 2011. 60910 62. 90. Von Lewinski M, Biau S, Erber R, Ille N, Aurich J, Faure JM, et al. Cortisol 73. Wang F, Tanaka M, Chonan S. Development of a PVDF piezopolymer release, heart rate and heart rate variability in the horse and its rider: sensor for unconstrained in-sleep cardiorespiratory monitoring. J Intell different responses to training and performance. Vet J. 2013;197(2):229– Mater Syst Struct. 2003;14(3):185–90. https:// doi. org/ 10. 1177/ 10453 32. https:// doi. org/ 10. 1016/j. tvjl. 2012. 12. 025. 89X03 01400 3006. 91. Schmied C, Boivin X, Waiblinger S. Stroking different body regions 74. Niizeki K, Nishidate I, Uchida K, Kuwahara M. Unconstrained cardiorespi- of dairy cows: effects on avoidance and approach behavior toward ratory and body movement monitoring system for home care. Med Biol humans. J Dairy Sci. 2008;91(2):596–605. https:// doi. org/ 10. 3168/ jds. Eng Comput. 2005;43(6):716–24. https:// doi. org/ 10. 1007/ BF024 30948.2007- 0360. 75. Koivistoinen T, Junnila S, Varri A, Koobi T. A new method for measuring 92. Sudhakara Reddy B, Sivajothi S. Analysis of base apex lead electrocar- the ballistocardiogram using EMFi sensors in a normal chair. In: 2004 diograms of adult buffaloes. J Dairy Vet Anim. 2015. https:// doi. org/ 10. Annu Int Conf IEEE Eng Med Biol Soc. vol. 1; 2004. p. 2026–9. https:// doi. 15406/ jdvar. 2015. 02. 00058. org/ 10. 1109/ IEMBS. 2004. 14035 96. 93. Mohr E, Langbein J, Nurnberg G. Heart rate variability: a noninva- 76. Kärki S, Lekkala J. A new method to measure heart rate with EMFi and sive approach to measure stress in calves and cows. Physiol Behav. PVDF materials. J Med Eng Technol. 2009;33(7):551–8. https:// doi. org/ 2002;75(1):251–9. https:// doi. org/ 10. 1016/ S0031- 9384(01) 00651-5. 10. 1080/ 03091 90090 30674 24. K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 21 of 21 94. Verheyen T, Decloedt A, De Clercq D, Deprez P, Sys S, van Loon G. 114. Tan Yf, Du L. Study on wavelet transform in the processing for ECG Electrocardiography in horses, part 1: how to make a good recording. signals. In: 2009 WRI World Congress on Software Engineering. vol. 4; Vlaams Diergeneeskd Tijdschr. 2010;79(5):331–6. 2009. p. 515–8. https:// doi. org/ 10. 1109/ WCSE. 2009. 89. 95. Janzekovic M, Vindis P, Stajnko D, Brus M. Polar sport tester for cattle 115. Martin Bland J, Altman D. Statistical methods for assessing agree- heart rate measurements. IntechOpen. 2010. https:// doi. org/ 10. 5772/ ment between two methods of clinical measurement. Lancet. 10356. 1986;327(8476):307–10. https:// doi. org/ 10. 1016/ S0140- 6736(86) 96. Tajik J, Samimi A, Shojaeepour S, Jarakani S. Analysis of base-apex 90837-8. lead electrocardiogram in clinically healthy Kermani sheep. Istanbul 116. Jackson P, Cockcroft P. Clinical examination of farm animals. John Wiley Universitesi Veteriner Fakultesi Dergisi. 2015;42(1):74–9. https:// doi. org/ & Sons; 2008. ISBN 0-632-05706-8. 10. 16988/ iuvfd. 2016. 77664. 117. Marjan J, Prišenk J, Mursec B, Vindis P, Stajnko D, Cus F. The art equip- 97. Costa CFd, Samesima N, Pastore CA. Cardiac mean electrical axis in thor- ment for measuring the horse’s heart rate. J Achiev Mater Manuf. oughbreds—standardization by the Dubois lead positioning system. 2010;41:180–6. PLoS ONE. 2017;12(1):e0169619. https:// doi. org/ 10. 1371/ journ al. pone. 118. Martinek R, Brablik J, Kolarik J, Ladrova M, Nedoma J, Kahankova R, 01696 19. et al. A Comparison between novel FPGA-based pad monitoring 98. Forin-Wiart MA, Enstipp MR, Maho LEY, Handrich Y. Why implantation system using ballistocardiography and the conventional systems for of bio-loggers may improve our understanding of how animals cope synchronization and gating of CMRI at 3 Tesla: a pilot study. IEEE Access. within their natural environment. Integr Zool. 2019;14(1):48–64. https:// 2020;8:4149–70. https:// doi. org/ 10. 1109/ ACCESS. 2019. 29632 21. doi. org/ 10. 1111/ 1749- 4877. 12364. 119. Mugdha AC, Rawnaque FS, Ahmed MU. A study of recursive least 99. Bjarnason Gunnarsson A, Arnason T, Oddgeirsson M, Sigmarsson squares (RLS) adaptive filter algorithm in noise removal from ECG sig- AB, Gunnarsson A. Validation of ECG-derived heart rate recordings nals. In: 2015 Int. Conf. Inform. Electron. Vis. (ICIEV ); 2015. p. 1–6. https:// in Atlantic cod (Gadus morhua L.) with an implantable data logging doi. org/ 10. 1109/ ICIEV. 2015. 73339 98 system. Anim Biotelemetry. 2019;7(1):13. https:// doi. org/ 10. 1186/ 120. Sharma B, Suji RJ. ECG denoising using Weiner filter and adaptive least s40317- 019- 0176-4. mean square algorithm. In: 2016 IEEE IEEE Int. Conf. Recent Trends 100. Svendsen E, Fore M, Okland F, Grans A, Hedger RD, Alfredsen JA, et al. Electron. Inf. Commun. Technol. RTEICT 2016-Proc. p. 53–7. https:// doi. Heart rate and swimming activity as stress indicators for Atlantic org/ 10. 1109/ RTEICT. 2016. 78077 81. salmon (Salmo salar). Aquaculture. 2021;531: 735804. https:// doi. org/ 10. 121. Kahankova R, Martinek R, Jaros R, Behbehani K, Matonia A, Jezewski M, 1016/j. aquac ulture. 2020. 735804. et al. A review of signal processing techniques for non-invasive fetal 101. McGaw IJ, Steell SC, Leeuwen TEV, Eliason EJ, Cooke SJ. Application of electrocardiography. IEEE Rev Biomed Eng. 2020;13:51–73. https:// doi. miniature heart rate data loggers for use in large free-moving decapod org/ 10. 1109/ RBME. 2019. 29380 61. crustaceans: method development and validation. Physiol Biochem 122. Barnova K, Martinek R, Jaros R, Kahankova R. Hybrid methods based on Zool. 2018;91(1):731–9. https:// doi. org/ 10. 1086/ 695839. empirical mode decomposition for non-Invasive fetal heart rate moni- 102. Palacios C, Plaza J, Abecia JA. A high cattle-grazing density alters circa- toring. IEEE Access. 2020;8:51200–18. https:// doi. org/ 10. 1109/ ACCESS. dian rhythmicity of temperature, heart rate, and activity as measured by 2020. 29802 54. implantable bio-loggers. Front Physiol. 2021. https:// doi. org/ 10. 3389/ 123. Martinek R, Kahankova R, Jaros R, Barnova K, Matonia A, Jezewski M, fphys. 2021. 707222. et al. Non-invasive fetal electrocardiogram extraction based on novel 103. Fuchs B, Sørheim KM, Chincarini M, Brunberg E, Stubsjøen SM, Bratberg- hybrid method for intrapartum ST segment analysis. IEEE Access. sengen K, et al. Heart rate sensor validation and seasonal and diurnal 2021;9:28608–31. https:// doi. org/ 10. 1109/ ACCESS. 2021. 30587 33. variation of body temperature and heart rate in domestic sheep. Vet 124. Graupe D, Veselinovic D. Blind adaptive filtering of speech from noise of Anim Sci. 2019;8: 100075. https:// doi. org/ 10. 1016/j. vas. 2019. 100075. unknown spectrum using a virtual feedback configuration. IEEE Trans 104. Selesnick IW, Burrus CS. Generalized digital Butterworth filter design. Speech Audio Process. 2000;8(2):146–58. https:// doi. org/ 10. 1109/ 89. IIEEE Trans Image Process. 1998;46(6):1688–94. https:// doi. org/ 10. 1109/ 824699. 78. 678493. 125. Yen KC, Zhao Y. Adaptive co-channel speech separation and recogni- 105. Guo-ping H, Ling-juan M, Hui D. A new method for effcient design of tion. IEEE Trans Speech Audio Process. 1999;7(2):138–51. https:// doi. Butterworth filter based on symbolic calculus. In: 2010 International org/ 10. 1109/ 89. 748119. Conference on Computer Application and System Modeling (ICCASM 126. Widrow B, Glover JR, McCool JM, Kaunitz J, Williams CS, Hearn RH, 2010). vol. 8; 2010. p. V8–111V8114. ISSN: 2161–9077. https:// doi. org/ 10. et al. Adaptive noise cancelling: principles and applications. Proc IEEE. 1109/ ICCASM. 2010. 56191 30. 1975;63(12):1692–716. https:// doi. org/ 10. 1109/ PROC. 1975. 10036. 106. Zhongshen L. Design and analysis of improved Butterworth low pass filter. In: 2007 8th Int Conf Electr Meas Inst. 2007. p. 1-7291732. https:// Publisher’s Note doi. org/ 10. 1109/ ICEMI. 2007. 43505 54 Springer Nature remains neutral with regard to jurisdictional claims in pub- 107. Jagtap SK, Uplane MD. The impact of digital filtering to ECG analysis: lished maps and institutional affiliations. Butterworth filter application. In: 2012 Int Conf Commun Inf Comput Technol. 2012. p. 1–6. https:// doi. org/ 10. 1109/ ICCICT. 2012. 63981 45. 108. Gaikwad KM, Chavan MS. Removal of high frequency noise from ECG signal using digital IIR Butterworth filter. In: 2014 IEEE Global Confer - ence on Wireless Computing Networking (GCWCN); 2014. p. 121–4. https:// doi. org/ 10. 1109/ GCWCN. 2014. 70308 61. Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : 109. Martinek R, Kahankova R, Martin B, Nedoma J, Fajkus M. A novel modu- lar fetal ECG STAN and HRV analysis: towards robust hypoxia detection. fast, convenient online submission Technol Health Care. 2019;27(3):257–87. https:// doi. org/ 10. 3233/ thorough peer review by experienced researchers in your ﬁeld THC- 181375. 110. McDonnell JTE, Bentley PM. Wavelet transforms: an introduction. Elec- rapid publication on acceptance tron Commun Eng J. 1994;6(4):175–86. https:// doi. org/ 10. 1049/ ecej: support for research data, including large and complex data types 19940 401. • gold Open Access which fosters wider collaboration and increased citations 111. Rioul O, Vetterli M. Wavelets and signal processing. IEEE Signal Process Mag. 1991;8(4):14–38. https:// doi. org/ 10. 1109/ 79. 91217. maximum visibility for your research: over 100M website views per year 112. Addison PS. Wavelet transforms and the ECG: a review. Physiol Meas. 2005;26(5):R155–99. https:// doi. org/ 10. 1088/ 0967- 3334/ 26/5/ R01. At BMC, research is always in progress. 113. Mukherjee A, Ghosh KK. An efficient wavelet analysis for ECG signal pro - Learn more biomedcentral.com/submissions cessing. In: 2012 Int. Conf. Inform. Electron. Vis. (ICIEV ); 2012. p. 411–5. https:// doi. org/ 10. 1109/ ICIEV. 2012. 63174 19. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Animal Biotelemetry Springer Journals http://www.deepdyve.com/lp/springer-journals/alternative-measurement-systems-for-recording-cardiac-activity-in-nKtM1Naw1b

Loading next page...

References (105)

C Lesimple, C Fureix, H Menguy, M Hausberger (2010)
Human direct actions may alter animal welfare, a study on horses (Equus caballus)
PLoS ONE, 5
M Joksimovic-Todorovic, V Davidovic, S Hristov, B Stankovic (2011)
Effect of heat stress on milk production in dairy cows
Biotechnol Anim Husb, 27
M Janzekovic, P Vindis, D Stajnko, M Brus (2010)
Polar sport tester for cattle heart rate measurements
IntechOpen
MA Forin-Wiart, MR Enstipp, LEY Maho, Y Handrich (2019)
Why implantation of bio-loggers may improve our understanding of how animals cope within their natural environment
Integr Zool, 14
DF Patterson, DK Detweiler, SA Glendenning (1965)
Heart sounds and murmurs of the normal horse*
Ann N Y Acad Sci, 127
U von Konig Borstel, S Euent, P Graf, S Konig, M Gauly (2011)
Equine behaviour and heart rate in temperament tests with or without rider or handler
Physiol Behav, 104
DK Detweiler, DF Patterson (1965)
The prevalence and types of cardiovascular disease in dogs
Ann N Y Acad Sci, 127
DK Detweiler, PW Macfarlane, A van Oosterom, O Pahlm, P Kligfield, M Janse, J Camm (2010)
The Mammalian Electrocardiogram: Comparative Features
Comprehensive Electrocardiology
E Mohr, J Langbein, G Nurnberg (2002)
Heart rate variability: a noninvasive approach to measure stress in calves and cows
Physiol Behav, 75
R Martinek, R Kahankova, B Martin, J Nedoma, M Fajkus (2019)
A novel modular fetal ECG STAN and HRV analysis: towards robust hypoxia detection
Technol Health Care, 27
D Graupe, D Veselinovic (2000)
Blind adaptive filtering of speech from noise of unknown spectrum using a virtual feedback configuration
IEEE Trans Speech Audio Process, 8
LJ Voss, DPG Bolton, BC Galland, BJ Taylor (2004)
Endotoxin effects on markers of autonomic nervous system function in the piglet: implications for SIDS
Neonatology, 86
C Nagel, M Melchert, C Aurich, J Aurich (2020)
Differences in endocrine and cardiac changes in mares and her fetus before, during, and after parturition in horses of different size
Animals, 10
M Kuwahara, A Hiraga, T Nishimura, H Tsubone, S Sugano (1998)
Power spectral analysis of heart rate variability in a horse with atrial fibrillation
J Vet Med Sci, 60
J Rushen, AM De Passillé, L Munksgaard (1999)
Fear of people by cows and effects on milk yield, behavior, and heart rate at milking
J Dairy Sci, 82
J Marjan, J Prišenk, B Mursec, P Vindis, D Stajnko, F Cus (2010)
The art equipment for measuring the horse’s heart rate
J Achiev Mater Manuf, 41
L Nie, D Berckmans, C Wang, B Li (2020)
Is continuous heart rate monitoring of livestock a dream or is it realistic? A review
Sensors, 20
M Anumukonda, SR Chowdhury, OA Postolache, SC Mukhopadhyay, KP Jayasundera, AK Swain (2017)
Heart sound sensing through MEMS microphone
Sensors for Everyday Life: Healthcare Settings. Smart Sensors Measurement and Instrumentation
CFd Costa, N Samesima, CA Pastore (2017)
Cardiac mean electrical axis in thoroughbreds—standardization by the Dubois lead positioning system
PLoS ONE, 12
CCBM Munsters, EK Visser, J van den Broek, MM van Sloet Oldruitenborgh-Oosterbaan (2013)
Physiological and behavioral responses of horses during police training
Animal, 7
A Esparza, M Ghamari, C Soltanpur, H Nazeran (2018)
A review on wearable photoplethysmography sensors and their potential future applications in health care
Int j biosens, 4
M Kuwahara, S Hashimoto, K Ishii, Y Yagi, T Hada, A Hiraga (1996)
Assessment of autonomic nervous function by power spectral analysis of heart rate variability in the horse
Auton Neurosci, 60
M Sinclair, C Fryer, CJC Phillips (2019)
The benefits of improving animal welfare from the perspective of livestock stakeholders across Asia
Animals, 9
B Manuja, A Manuja, P Aich (2012)
Stress and its impact on farm animals
Front Biosci, E4
J Tajik, A Samimi, S Shojaeepour, S Jarakani (2015)
Analysis of base-apex lead electrocardiogram in clinically healthy Kermani sheep
Istanbul Universitesi Veteriner Fakultesi Dergisi, 42
P Cossio, JA Berreta, HE Mosso, I Perianes (1954)
Ballistocardiogram in experimental myocardial lesions
Cardiology, 24
PJ Pretorius, M Terblanche, JJ van der Walt (1968)
Ballistocardiography in some domestic animals
Bibl Cardiol, 21
R Kahankova, R Martinek, R Jaros, K Behbehani, A Matonia, M Jezewski (2020)
A review of signal processing techniques for non-invasive fetal electrocardiography
IEEE Rev Biomed Eng, 13
ER Carvalho, GG Franco (2018)
Heart rate variability as a non-invasive method to assess welfare in dairy cows. Approaches in poultry
Behav Sci, 3
D Mésangeau, D Laude, JL Elghozi (2000)
Early detection of cardiovascular autonomic neuropathy in diabetic pigs using blood pressure and heart rate variability
Cardiovasc Res, 45
CM Brown, JR Holmes (1979)
Phonocardiography in the horse: 2. The relationship of the external phonocardiogram to intracardiac pressure and sound
Equine Vet J, 11
R Martinek, R Kahankova, R Jaros, K Barnova, A Matonia, M Jezewski (2021)
Non-invasive fetal electrocardiogram extraction based on novel hybrid method for intrapartum ST segment analysis
IEEE Access, 9
A Bjarnason Gunnarsson, T Arnason, M Oddgeirsson, AB Sigmarsson, A Gunnarsson (2019)
Validation of ECG-derived heart rate recordings in Atlantic cod (Gadus morhua L.) with an implantable data logging system
Anim Biotelemetry, 7
IW Selesnick, CS Burrus (1998)
Generalized digital Butterworth filter design
IIEEE Trans Image Process, 46
Y Yao, S Shin, A Mousavi, CS Kim, L Xu, R Mukkamala (2019)
Unobtrusive estimation of cardiovascular parameters with limb ballistocardiography
Sensors, 19
DK Detweiler (1964)
Generic aspects of cardiovascular diseases in animals
Circulation, 30
M Caroprese, M Albenzio, A Marzano, L Schena, G Annicchiarico, A Sevi (2010)
Relationship between cortisol response to stress and behavior, immune profile, and production performance of dairy ewes
J Dairy Sci, 93
K Barnova, R Martinek, R Jaros, R Kahankova (2020)
Hybrid methods based on empirical mode decomposition for non-Invasive fetal heart rate monitoring
IEEE Access, 8
SD McBride, DS Mills (2012)
Psychological factors affecting equine performance
BMC Vet Res, 8
B Voss, E Mohr, H Krzywanek (2002)
Effects of aqua-treadmill exercise on selected blood parameters and on heart-rate variability of horses
J Vet Med A Physiol Pathol Clin Med, 49
E Zucca, F Ferrucci, V Di Fabio, C Croci, E Ferro (2003)
The use of electrocardiographic recording with Holter monitoring during treadmill exercise to evaluate cardiac arrhythmias in racehorses
Vet Res Commun, 27
J Langbein, G Nürnberg, G Manteuffel (2004)
Visual discrimination learning in dwarf goats and associated changes in heart rate and heart rate variability
Physiol Behav, 82
A Henningsen, TG Czekaj, B Forkman, M Lund, AS Nielsen (2018)
The relationship between animal welfare and economic performance at farm level: a quantitative study of Danish pig producers
J Agric Econ, 69
M Von Lewinski, S Biau, R Erber, N Ille, J Aurich, JM Faure (2013)
Cortisol release, heart rate and heart rate variability in the horse and its rider: different responses to training and performance
Vet J, 197
J Rushen, L Munksgaard, PG Marnet, AM DePassillé (2001)
Human contact and the effects of acute stress on cows at milking
Appl Anim Behav Sci, 73
PS Addison (2005)
Wavelet transforms and the ECG: a review
Physiol Meas, 26
R Martinek, J Nedoma, M Fajkus, R Kahankova, J Konecny, P Janku (2017)
A phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring
Sensors, 17
E Svendsen, M Fore, F Okland, A Grans, RD Hedger, JA Alfredsen (2021)
Heart rate and swimming activity as stress indicators for Atlantic salmon (Salmo salar)
Aquaculture, 531
A Lanata, A Guidi, P Baragli, G Valenza, EP Scilingo (2015)
A novel algorithm for movement artifact removal in ECG signals acquired from wearable systems applied to horses
PLoS ONE, 10
E Gebregeziabhea (2015)
The effect of stress on productivity of animals: a review
J Biol Agric, 5
M Stewart, HM Shepherd, JR Webster, JR Waas, LM McLeay, KE Schutz (2013)
Effect of previous handling experiences on responses of dairy calves to routine husbandry procedures
Animal, 7
JD Perkins, IM Bowen, RW Else, CM Marr, IG Mayhew (2000)
Functional and histopathological evidence of cardiac parasympathetic dysautonomia in equine grass sickness
Vet Rec, 146
A Boissy, P Le Neindre (1997)
Behavioral, cardiac and cortisol responses to brief peer separation and reunion in cattle
Physiol Behav, 61
E Von Borell, J Langbein, G Després, S Hansen, C Leterrier, J Marchant-Forde (2007)
Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals—A review
Physiol Behav, 92
SM Stubsjøen, M Knappe-Poindecker, J Langbein, T Fjeldaas, J Bohlin (2015)
Assessment of chronic stress in sheep (part II): exploring heart rate variability as a non-invasive measure to evaluate cardiac regulation
Small Rumin Res, 133
DK Detweiler, H Luginbuhl, JW Buchanan, DF Patterson (1968)
The natural history of acquired cardiac disability of the dog*
Ann N Y Acad Sci, 147
DK Detweiler (1962)
Comparative cardiology and cardiovascular disease
J Clin Epidemiol, 15
CM Brown, JR Holmes (1979)
Phonocardiography in the horse: 1. The intracardiac phonocardiogram
Equine Vet J, 11
D Borchevkin (2015)
Method of photoplethysmography diagnostics of domesticated animals cardiovascular diseases
J Ve. Sci Technol
EL McConachie, S Giguere, G Rapoport, MH Barton (2016)
Heart rate variability in horses with acute gastrointestinal disease requiring exploratory laparotomy
J Vet Emerg Crit Care, 26
T Verheyen, A Decloedt, D De Clercq, P Deprez, S Sys, G van Loon (2010)
Electrocardiography in horses, part 1: how to make a good recording
Vlaams Diergeneeskd Tijdschr, 79
R Wijshoff, TVd Sar, R Aarts, P Woerlee, G Noordergraaf (2013)
Potential of photoplethysmography to guide pulse checks during cardiopulmonary resuscitation: observations in an animal study
Resuscitation, 84
MS Khan, N Zhao, AL Sica, N Gootman, PM Gootman (2001)
Changes in R-R and Q-T intervals following cardiac vagotomy in neonatal swine
Exp Biol Med, 226
GD Gargiulo, RW Shephard, J Tapson, AL McEwan, P Bifulco, M Cesarelli (2012)
Pregnancy detection and monitoring in cattle via combined foetus electrocardiogram and phonocardiogram signal processing
BMC Vet Res, 8
H Hopster, HJ Blokhuis (1994)
Validation of a heart-rate monitor for measuring a stress response in dairy cows
Can J Anim Sci, 74
M Malik (1996)
Heart rate variability
Ann Noninvasive Electrocardiol, 1
L Kovács, FL Kézér, V Jurkovich, M Kulcsár-Huszenicza, J Tőzsér (2015)
Heart rate variability as an indicator of chronic stress caused by lameness in dairy cows
PLoS ONE, 10
G Deprés, I Veissier, A Boissy (2002)
Effect of autonomic blockers on heart period variability in calves: evaluation of the sympathovagal balance
Physiol Res, 51
JW Buchanan (2013)
The history of veterinary cardiology
J Vet Cardiol, 15
B Sudhakara Reddy, S Sivajothi (2015)
Analysis of base apex lead electrocardiograms of adult buffaloes
J Dairy Vet Anim
L Kovács, V Jurkovich, M Bakony, O Szenci, P Póti, J Tozser (2014)
Welfare implication of measuring heart rate and heart rate variability in dairy cattle: literature review and conclusions for future research
Animal, 8
JTE McDonnell, PM Bentley (1994)
Wavelet transforms: an introduction
Electron Commun Eng J, 6
JA Behar, AA Rosenberg, I Weiser-Bitoun, O Shemla, A Alexandrovich, E Konyukhov (2018)
PhysioZoo: a novel open access platform for heart rate variability analysis of mammalian electrocardiographic data
Front Physiol
K Niizeki, I Nishidate, K Uchida, M Kuwahara (2005)
Unconstrained cardiorespiratory and body movement monitoring system for home care
Med Biol Eng Comput, 43
IJ McGaw, SC Steell, TEV Leeuwen, EJ Eliason, SJ Cooke (2018)
Application of miniature heart rate data loggers for use in large free-moving decapod crustaceans: method development and validation
Physiol Biochem Zool, 91
R Martinek, J Brablik, J Kolarik, M Ladrova, J Nedoma, R Kahankova (2020)
A Comparison between novel FPGA-based pad monitoring system using ballistocardiography and the conventional systems for synchronization and gating of CMRI at 3 Tesla: a pilot study
IEEE Access, 8
B Widrow, JR Glover, JM McCool, J Kaunitz, CS Williams, RH Hearn (1975)
Adaptive noise cancelling: principles and applications
Proc IEEE, 63
L Gygax, I Neuffer, C Kaufmann, R Hauser, B Wechsler (2008)
Restlessness behaviour, heart rate and heart-rate variability of dairy cows milked in two types of automatic milking systems and auto-tandem milking parlours
Appl Anim Behav Sci, 109
TR Rietmann, M Stauffacher, P Bernasconi, JA Auer, MA Weishaupt (2004)
The association between heart rate, heart rate variability, endocrine and behavioural pain measures in horses suffering from laminitis
J Vet Med A Physiol Pathol Clin Med, 51
K Hagen, J Langbein, C Schmied, D Lexer, S Waiblinger (2005)
Heart rate variability in dairy cows—influences of breed and milking system
Physiol Behav, 85
M Parker, D Goodwin, RA Eager, ES Redhead, DJ Marlin (2009)
Comparison of Polar® heart rate interval data with simultaneously recorded ECG signals in horses
Comp Exerc Physiol, 6
Y Cui, M Zhang, J Li, H Luo, X Zhang, Z Fu (2019)
WSMS: wearable stress monitoring system based on IoT multi-sensor platform for living sheep transportation
Electronics, 8
F Wang, M Tanaka, S Chonan (2003)
Development of a PVDF piezopolymer sensor for unconstrained in-sleep cardiorespiratory monitoring
J Intell Mater Syst Struct, 14
S Leng, RS Tan, KTC Chai, C Wang, D Ghista, L Zhong (2015)
The electronic stethoscope
Biomed Eng Online, 14
J Martin Bland, D Altman (1986)
Statistical methods for assessing agreement between two methods of clinical measurement
Lancet, 327
O Rioul, M Vetterli (1991)
Wavelets and signal processing
IEEE Signal Process Mag, 8
KC Yen, Y Zhao (1999)
Adaptive co-channel speech separation and recognition
IEEE Trans Speech Audio Process, 7
M Minero, E Canali (2009)
Welfare issues of horses: an overview and practical recommendations
Ital J Anim Sci, 8
K Vörös, I Nolte, S Hungerbuhler, J Reiczigel, JP Ehlers, G Tater (2011)
Sound recording and digital phonocardiography of cardiac murmurs in dogs by using a sensor-based electronic stethoscope
Acta Vet Hung, 59
F Shaffer, JP Ginsberg (2017)
An overview of heart rate variability metrics and norms
Front Public Health, 5
L Bennet, AJ Gunn (2009)
The fetal heart rate response to hypoxia: insights from animal models
Clin Perinatol, 36
AS Chamove, OJE Crawley-Hartrick, KJ Stafford (2002)
Horse reactions to human attitudes and behavior
Anthrozoos, 15
AAR Kamal, JB Harness, G Irving, AJ Mearns (1989)
Skin photoplethysmography—a review
Comput Methods Programs Biomed, 28
B Fuchs, KM Sørheim, M Chincarini, E Brunberg, SM Stubsjøen, K Bratbergsengen (2019)
Heart rate sensor validation and seasonal and diurnal variation of body temperature and heart rate in domestic sheep
Vet Anim Sci, 8
S Kärki, J Lekkala (2009)
A new method to measure heart rate with EMFi and PVDF materials
J Med Eng Technol, 33
C Palacios, J Plaza, JA Abecia (2021)
A high cattle-grazing density alters circadian rhythmicity of temperature, heart rate, and activity as measured by implantable bio-loggers
Front Physiol
C Turbill, T Ruf, T Mang, W Arnold (2011)
Regulation of heart rate and rumen temperature in red deer: effects of season and food intake
J Exp Biol, 214
I Janczarek, W Kedzierski, A Stachurska, I Wilk, R Kolstrung, K Strzelec (2016)
Autonomic nervous system activity in purebred Arabian horses evaluated according to the low frequency and high frequency spectrum versus racing performance
Acta Vet Brno, 85
A Lenoir, DS Trachsel, M Younes, E Barrey, C Robert (2017)
Agreement between electrocardiogram and heart rate meter is low for the measurement of heart rate variability during exercise in young endurance horses
Front Vet Sci
S Vetter, L Vasa, L Ózsvári (2014)
Economic aspects of animal welfare
Acta Polytech Hung, 11
B Cugmas, E Struc, J Spigulis (2019)
Photoplethysmography in dogs and cats: a selection of alternative measurement sites for a pet monitor
Physiol Meas, 40
A Guidi, A Lanata, P Baragli, G Valenza, EP Scilingo (2016)
A wearable system for the evaluation of the human horse interaction: a preliminary study
Electronics, 5
M Stewart, GA Verkerk, KJ Stafford, AL Schaefer, JR Webster (2010)
Noninvasive assessment of autonomic activity for evaluation of pain in calves, using surgical castration as a model
J Dairy Sci, 93
C Schmied, X Boivin, S Waiblinger (2008)
Stroking different body regions of dairy cows: effects on avoidance and approach behavior toward humans
J Dairy Sci, 91
E Bartolomé, MS Cockram (2016)
Potential effects of stress on the performance of sport horses
J Equine Vet Sci, 40

Publisher: Springer Journals
Copyright: Copyright © The Author(s) 2022
eISSN: 2050-3385
DOI: 10.1186/s40317-022-00286-y
Publisher site: See Article on Publisher Site

Abstract

Monitoring and assessing cardiac activity in animals, especially heart rate variability, has been gaining importance in the last few years as an indicator of animal health, well-being and physical condition. This pilot study tested the sen- sors based on ballistocardiography sensing the mechanical vibrations caused by the animal’s cardiovascular system, which have proved useful in measuring cardiac activity in humans. To verify the accuracy of these measurement sys- tems, the conventional measurements based on electrocardiography were carried out and the outcomes were com- pared. The main objectives were to verify the suitability of these sensors in measuring cardiac activity in animals, to determine the advantages and disadvantages of these sensors, and to identify future challenges. Measurements were performed on various animals, specifically a goat, a cow, a horse, and a sheep. Electrocardiographic measurement, which has demonstrated high accuracy in procedures for animals, was used as the study’s gold standard. A disadvan- tage of this method, however, is the long time required to prepare animals and shear spots to attach electrodes. The accuracy of a ballistocardiographic sensor was compared to reference electrocardiographic signals based on Bland– Altman plots which analysed the current heart rate values. Unfortunately, the ballistocardiographic sensor was highly prone to poor adhesion to the animal’s body, sensor movement when the animal was restless, and motion artefacts. Ballistocardiographic sensors were shown only to be effective with larger animals, i.e., the horse and the cow, the size of these animals allowing sufficient contact of the sensor with the animal’s body. However, this method’s most signifi- cant advantage over the conventional method based on electrocardiography is lower preparation time, since there is no need for precise and time-demanding fixation of the sensor itself and the necessity of shaving the animal’s body. Keywords: Animal electrocardiography (ECG), Heart rate variability (HRV ), Heart rate (HR), Animal welfare, Stress, Veterinary monitoring, Ballistocardiography (BCG), Farm animals in 1819 by French doctor René–Théophile–Hyacinthe Introduction Laënnec [1]. However, up to the early 1950s, only a few Monitoring cardiac activity in animals is currently an articles had discussed monitoring cardiac activity in important tool in the assessment of an animal’s over- horses and other animals, see [2]. A great change came in all health. The first mention of the potential diagnos - the 1960s, when David K. Detweiler, known as the “father tic importance of heart sounds in animals was written of veterinary cardiology”, brought animal cardiology to the forefront of veterinary practice [1]. Detweiler focused mainly on cardiovascular diseases in dogs, horses and *Correspondence: radana.kahankova@vsb.cz Department of Cybernetics and Biomedical Engineering, VSB— cattle [2–7]. He examined animals with systemic auscul- Technical University of Ostrava, Ostrava, Czechia tation and electrocardiography (ECG). Over time, special Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 2 of 21 cardiologic procedures were introduced and recognised the animal, applying gel or otherwise address the con- globally, and animal cardiology became an essential part ductivity between the electrode and the skin. Moreo- of veterinary practice [1]. ver, when acquiring the ECG signal, several electrodes Over the last few years, clinical assessment of cardiac need to be placed in different locations of the animal’s activity in animals has been gaining importance, not body, whereas to obtain BCG signal, a single sensor can only with regard to physiological and pathophysiological be used. When incorporated into a saddle or belt, this indicators, but also as an indicator of an animal’s men- method could offer a quick, simple, and durable attach - tal health and well-being [8–13]. The stress levels can be ment to the animal and become an ideal tool for cardiac measured using different physiological indicators, such monitoring in animals. This paper, therefore, aims to test as heart rate (HR) or serum levels of various stress hor- a prototype of the BCG-based measuring system to eval- mones (e.g., cortisol) [14–16]. The advantage of using uate its accuracy and suitability for this task. the HR (or heart rate variability, HRV) is that it can be monitored non-invasively and continually. The other Background mentioned methods require the blood to be collected Developments in sensing technology have made it pos- repetitively which is another stress factor for animals and sible to accurately monitor vital signs in humans, but in can lead to distorted results [10]. Thus, recording HR as the case of animals, measurement is more complicated. an indicator of stress seems to be a better option. This is mainly due to the different body structure and The HR assessment is used to identify stress in animals unpredictability of their behaviour. By monitoring the based on the assumption that HR reflects the activity of vital functions of animals, it is possible to obtain a large the sympathetic nerve fibres. However, changes in HR amount of valuable information about their physical are not exclusively in response to stress, i.e., a negative and mental health. Ensuring the welfare of animals and or painful stimulus, but also in response to other behav- preventing the occurrence of stress is associated with ioural and physiological influences, such as low oxygen their improved performance and thus positive economic levels [11], physical activity, temperature [12], illness [37, impact [24–36]. The goal of the researchers is, therefore, 38], and overall welfare of the animal [13]. Therefore, to design and put into practice an automated, non-inva- interpretation of mental state based solely on HR meas- sive system enabling long-term monitoring of animals. urement is not always unambiguous [17]. The HRV may This section summarizes examples of the practical use be a suitable alternative as a quantitative indicator of ani- of measuring animal cardiac activity, including monitor- mal health and well-being. This parameter represents the ing techniques and evaluation parameters that have been comprehensive degree of a physiological function derived used in the past. from the heart cycle. It expresses the variability which exists between consecutive heart rhythms and is meas- Cardiac monitoring in animals ured from the R wave of the QRS complex, which allows Changes in cardiac activity can be assessed according to more accurate and detailed assessment of the physical or numerous parameters with regard to time or frequency. mental condition of the animal [18–22]. In the time domain, the immediate HR can be deter- The HR can be measured by various techniques for mined at any point in time or in the intervals between cardiac activity monitoring, see section II.B. Among the consecutive QRS complexes, i.e., the normal-to-normal most commonly used methods is electrocardiography, (NN) intervals [20, 21]. The average NN interval, the which is considered a gold standard in cardiac monitor- average HR, or the difference between the longest and ing in humans. However, this method faces multiple chal- the shortest NN intervals are also simple parameters lenges in animal monitoring, especially due to presence which can be determined in the time domain. More of fur and differences in their anatomy, see section II.C. complex parameters can be calculated according to the Therefore, this article presents an alternative approach immediate HR, the direct measurement of NN intervals, in measuring HR, so-called ballistocardiography (BCG), or the differences between NN intervals [20]. Several which is based on monitoring body movements caused by studies [22–37] have reported that HRV or HR param- accelerated blood flow inside large vessels. The method eters are objective indicators of the mental and physical proved to be efficient in HR monitoring in humans and is well-being of animals during veterinary and breeding considered low-cost, simple to prepare, and easy to oper- practices. Examples of specific uses of measurements of ate [23]. HRV or HR parameters in animals are summarised in the For cardiac monitoring in animals, the BCG method following subsection and in Table 1. provides several benefits over the conventional ECG- The effect of the environment and the presence of based approach. The BCG has lower requirements on humans on HR and HRV parameters in animals (cows surface quality than ECG as it does not require shaving and horses) was investigated in [19, 24–30]. In cows, K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 3 of 21 Table 1 Examples of the use of HRV or HR measurement in animals Author, source Animal Use Results Rushen et al. [24] Cattle Impact of the environment and human presence Higher HR and lower milk production in isolate cows on the quantity of produced milk Rushen et al. [25] Cattle Impact of human behaviour in presence of cows Higher HR and lower milk production, if the cows on the quantity of produced milk were in the presence of a person who behaved aversively Kovacs et al. [37] Cattle Assessment of health (lameness) Lower HR and higher HRV in cows with lameness Stubsjøen et al. [38] Sheep Assessment of health (lameness) Higher HRV in sheep with lameness Mesangeau et al. [44] Miniature pigs Model for early detection of cardiovascular auto- Higher resting HR and lower HRV in pigs with cardio- nomic neuropathy vascular autonomic neuropathy Voss et al. [45] Pigs Model for sudden infant death syndrome Lower HRV in pigs during external warming Guidi et al. [19] Horse Estimation of the human–horse interaction for Quantitative measure of the human-horse interac- equine assisted therapy tion using HRV is viable Perkins et al. [40] Horse Assessment of health (grass sickness) Lower HRV in horses with grass sickness Rietmann et al. [41] Horse Assessment of pain after short-term and long-term Decrease of high-frequency component of HRV is treatment of laminitis attributable to an increase in pain Kuwahara et al. [42] Horse Assessment of health (atrial fibrillation) Higher low-frequency and high-frequency power of HRV in horses with atrial fibrillation McConachie et al. [43] Horse Assessment of health (acute gastrointestinal Lower HRV in horses with acute gastrointestinal disease) disease Janczarek et al. [47] Horse Impact on racing performance Higher HRV, better racing results Munsters et al. [49] Horse Impact of police training on welfare No significant changes in HR and HRV were detected, training is not stressful for both experi- enced and inexperienced horses Voss et al. [48] Horse Assessment o Rise of HR and decrease of HRV was observed with f degree of load during training increasing burden level König von Borstel et al. [30] Horse Temperament test HR and HRV were lowest when the horses were led and/or no human was present Nagel et al. [39] Horse Monitoring progress of delivery Higher mother’s HR immediately before delivery, higher HRV only during delivery these parameters were most frequently monitored in stress to the animal, this can be also assessed by the HR connection with the volume of milk the animals pro- and HRV parameters [49]. duce [24–27]. The results of the studies show that the Further investigations were focused on monitoring stress caused by the change of environment, such as animals’ well-being, especially early detection of illness their transfer to isolated stable [24], or adverse human or pain in cattle, sheep and horses. The research top - behaviour [25], lead to increase in HR and reduction ics covered, for example, relation of HR changes caused milk yield. Human–horse interaction can be estimated by stress due to lameness in cow [37] and sheep [38], through the assessment of HRV, since the presence of changes of HRV during labor in mares [39], or varia- rider or handler affects the horses’ inner behaviour tions of HRV in horses with diseases, such as grass [19, 28–30]. These findings can be applied in equine- sickness [40], laminitis [41], atrial fibrillation [42], or assisted therapy or in monitoring the effect of thera - gastrointestinal disease treated by exploratory lapa- peutic horseback riding [30]. rotomy [43]. The findings show that HRV can be used The same parameters (HRV and HR) were monitored to differentiate between healthy and sick animals [40, in horses to improve the quality of training and improve 42] but also assess presence of pain [41] and severity of the performance of the animals [47–49]. In terms of the illness [38], since its development is associated with performance in racing horses, the higher values of HRV significant increase in the HRV parameters [38]. Fur - parameters, the better results [47]. The training can thermore, a reduction of HRV after surgery was used to be thus adjusted (by, e.g., selecting a proper exercise) predict a non-survival and thus HRV can be used both according to the parameters and the aerobic work load for diagnostic and prognostic purposes [43]. As sug- needed for a given horse [48]. Moreover, for the train- gested in [41], such HRV-based indicator would be con- ing to be effective, it should not induce an unnecessary sidered a more practical and less expensive assessment Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 4 of 21 than other available tools, such as stress hormone analysis. Measurements of HRV and HR parameters in animals were also used in research that aimed to model human ECG diseases [44–46]. Several studies used pigs to model dis- Q S eases, such as sudden infant death syndrome [45], or cardiovascular autonomic neuropathy in subjects with L BCG diabetes [44]. The monitored HRV parameters showed significant changes correlating with symptoms of the K investigated diseases and HRV has thus proved as a suit- able method for early detection and therapeutic strategy development [44, 45]. Besides the HR evaluation, it is possible to analyze PPG the cardiac activity in animals using more sophisticated S1 S2 parameters. For example, in [50], the authors focused on evaluating the cardiac activity in calves using the RR PCG interval mean, RR interval standard deviation (SDRR), and root mean square of successive differences (RMSSD) Fig. 1 Example of ECG, BCG, PPG, and PCG waveforms. Compared to parameters in the time domain. The spectral band power the R peak in ECG, the other signals are physiologically delayed by the (VLF, LF, HF) and the LF/HF ratio were calculated in the pulse transit time [81] frequency domain. In [51], the mean interbeat interval, RMSSD, and pNN50 parameters were studied in horses. ods use, for example, a piezoelectric crystal placed on In [37], HRV in dairy cows was measured as an indica- the head of a metal shaft which contacts a membrane tor of chronic stress caused by lameness. The animals [67], principle of induction [68], or non-invasive were assessed according to RMSSD, HF, LF/HF, geomet- fibre-optics [69]. The PCG method was used for the ric measurements (triangular interpolation of normal to HR monitoring in dogs [53], horses [54, 55], and to normal, R–R triangular index), Poincaré measurements, monitor pregnancy in cattle [56]. and non-linear measurements (Shannon entropy, short- b) Photoplethysmography—uses a light source and a term fluctuations in HRV, long-term fluctuations in HRV, photodetector to measure variations of volume in correlation dimension). A more detailed overview of blood circulation [70]. A light source shines light HRV parameters can be found in [20, 21, 52]. into tissue and a photodetector then measures the quantity of reflected light, which is proportional Acquisition of cardiac activity to any change in blood volume. The most common Electrocardiography (ECG), ballistocardiography (BCG), PPG sensors use infrared LEDs or green LEDs as the photoplethysmography (PPG) and phonocardiography main light source [70, 71]. In veterinary medicine (PCG) are among the most frequently used techniques and research, the PPG was tested in guided pulse which have proved effective in monitoring cardiac activ - checks during cardiopulmonary resuscitation [57], or ity in humans. An example of all four curves is shown in to diagnose cardiovascular diseases in domestic ani- Fig. 1. The ECG-based monitoring is the most common mals [58]. The use of PPG for continuous monitoring method in both human and animal cardiac monitoring, of cardiac activity was assessed in dogs and cats [59], but only limited number of studies [53–64] have explored farm animals [60] and stress monitoring in sheep the application of the alternative methods in measuring during transport [61]. Moreover, besides external the cardiac activity of animals. The basic principles of the PPG sensors, there are also internal sensors available, individual techniques and their practical use in veteri- which enable continual subcutaneous data collection. nary can be summarised as follows: For example, the authors in [62] used an implantable but extravascular sensor for measuring blood oxygen a) Phonocardiography—a passive, low-cost method and saturation in sheep. one of the oldest techniques for recording the sounds c) Ballistocardiography—method based on capturing of the heart. The method captures the heart sounds the body movements caused by accelerated blood produced by the opening and closing of the heart flow inside large vessels [72]. Various types of sen - valves and blood flow [65]. The simplest method of sors can be used to capture these movements and are capturing heart sounds is in the use of a microphone able to generate a voltage as a result of mechanical placed on the surface of the body [66]. Other meth- K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 5 of 21 deformation or changes in pressure. For example, a these specialized devices is an obstacle in broader use in piezopolymer film pressure sensors [73, 74], elec- research. tromechanical film-based sensors [75, 76], hydraulic Many authors [82–95] have, therefore, opted for the use sensors [77, 78], hydraulic sensors or fibre optic sen - of ECG-based HR monitors in researching animal HRV, sors [79, 80]. So far, BCG measurements were tested since these are more affordable. These devices record the in domesticated animals [63] and in dogs [64]. times between the two main depolarisation waves (R–R d) Electrocardiography—captures the electric poten- intervals) and then convert these data into HR values. tials produced by the heart that are projected on the HR monitors produced by Polar Electro Oy (Kempele, body’s surface using electrodes placed on the skin. Finland) are frequently used. The Polar S810i model was This method is well known and used as a gold stand - used, for example in [82, 85–89]; the Polar Vantage NV ard in medicine. However, this technique faces many model was used in [93]; the Polar Sport Tester monitor obstacles when measured in animals, such as rela- was used in [13, 95]; and the Polar RS800 monitor was tively demanding preparation and low quality of the used in [37, 38]. However, these devices are not capable ECG signal due to numerous artefacts. This will be of recording all aspects of cardiac activity, and this leads discussed in detail in following section. to a loss of clinically important information which can otherwise be obtained using ECG. The absence of any gold standard for measurement and electrode placement is one of the major obstacles in cap- Standard monitoring and its challenges turing ECG in animals. Measurement using base-apex The ECG based monitoring requires relatively demand - leads (the electrodes are placed along the mean electri- ing preparation in animals [82]. First, an optimal location cal axis; one electrode on the lower thorax between the to fix the electrodes must be identified to prevent move - elbow and xiphoid and a second in the region between ment or removal of the electrodes; the selected locations the lower neck and the withers), while the animal stands on the animal’s body then need to be shaved. A disadvan- is performed most frequently in cattle, goats, sheep, and tage is that the quality of the ECG signal is often dete- horses [92, 94–96]. Examples of electrode placement riorated by artefacts recorded simultaneously with the during ECG capture are summarised below (Table 2 and useful signal. When ECG data is measured in an animal, Fig. 2). the motion artefacts are mainly caused by the animal’s With the use of the base-apex lead, ECG monitor- restlessness, since the measurement procedure itself is ing was performed on cows in [95]. ECG was captured rather stressful to animals [83, 84]. Additional process- using disposable adhesive electrodes and gel. The nega - ing of the ECG signal requires suitably selected filtering tive electrode was placed in the caudal angle of the left methods to obtain as precise information about the ani- scapula; the right electrode was placed in the left inter- mal’s health as possible. Recording for at least 5 min dur- costal region caudally to the olecranon; and the ground ing stationary conditions is recommended for adequate electrode was placed in the region of the left paralumbar analysis of HRV [20]. fossa. Similarly, the base-apex lead was also successfully Many devices for short-term and long-term monitor- used in [96] for ECG-based monitoring of sheep. Alli- ing exist for the recording, storage and analysis of human gator-type electrodes attached to the skin were used for ECG data. Unfortunately, this is relatively costly equip- measurement. The negative electrode was attached to ment adapted to the analysis of human cardiac activity the left side of the neck in the jugular furrow area; the [22]. There are only a few commercially available external positive electrode was positioned at the fifth intercostal ECG monitoring devices designed for use in veterinary space; and the ground electrode was placed away from medicine on the market. These include, for example, the these two electrodes. Televet 100 ECG monitor produced by Engel Engineer- In [50], the ECG was measured with the use of adhe- ing Service GmbH (Heusenstamm, Germany), which sive ECG electrodes attached to the shaved skin of a calf. can be used for continuous ECG monitoring in large and Secured with a band of elastic, one electrode was placed small animals. Another is the 6-channel veterinary ECG along the sternum, and the second was placed above the ek3008monitor with connection to the smartphone is right scapula. In [97], the authors compared the base- provided by Chip Ideas Electronics, S.L. eKuore (Valen- apex lead method and the Dubois method of monitor- cia, Spain). The Veterinary ECG/Heart Monitor Universal ing cardiac activity in horses. Placement of the four ECG Adapter manufactured by Woodley Equipment Company electrodes in the Dubois method was identified as a more Ltd (Bolton, UK) enabling wireless communication with a precise and reliable option. In this case, one electrode mobile application or the 3-channel veterinary ECG sys- was placed on the left scapula, the second electrode on tem ECG-T3V is manufactured by Shinova Medical Co., the right scapula, the third in the region of the sternum Ltd (Shanghai, China). However, higher purchase price of Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 6 of 21 Table 2 Summary of ECG electrode placement for the measurement of cardiac activity in animals Author, source Animal Electrode placement system Number of ECG electrode placement electrodes Hopster et al. [13] Cattle – 3 Electrodes placed on the left front leg, left part of the abdomen, and left hind leg Konold et al. [95] Cattle Base-apex 3 Negative electrode: left scapula, positive electrode: left intercostal space ground, electrode: left paralumbar fossa Depres et al. [50] Cattle – 2 Electrodes placed along the sternum and on the right scapula Tajik et al. [96] Sheep Base-apex 3 The negative electrode: left side of the neck on the jugular furrow, positive electrode: fifth intercostal space, ground electrode: away from the other two electrodes Guidi et al. [19] Horse Modified base-apex 2 Electrodes integrated into an elastic band and placed in the area behind Lanata et al. [83] the left front leg Lanata et al. [84] Lenoir et al. [57] Horse – 5 The positive electrode: left side of the thorax behind the olecranon, nega- Zucca et al. [88] tive electrode: left side of the chest behind the withers, ground electrode: above the left mid-thorax between these electrodes Costa et al. [97] Horse Dubois method 4 Measuring electrodes: right scapula, left scapula, region of the sternum (over the xiphoid); ground electrode: left front leg Rightside Leftside Leftside Left side GND GND 2 2 c) d) a) b) Leftside Leftside Rightside Left side 3 12 GND 2 2 4 GND e) f) g) Fig. 2 Example of ECG electrode placement for the measurement of cardiac activity in animals according to: a Hopster et al. [13], b Konold et al. [95], c Depres et al. [50], d Tajik et al. [96], e Guidi et al. [19], f Zucca et al. [88], and g Costa et al. [97] (over the xiphoid), and the ground electrode was posi- of elastic and two textile electrodes integrated into the tioned on the left front leg. Finally, in [57] and [88], the belt. Both electrodes were placed in the area behind the ECG was acquired using five electrodes in horses. The left front leg (modified base-apex system). In [19], the positive electrodes of both leads were placed on the left quality of the recorded ECG signal was compared (espe- side of the thorax behind the olecranon; the negative cially in relation to motion artefacts) to a signal recorded electrodes of both leads were positioned on the left side simultaneously with conventional Ag/AgCl electrodes. of the chest behind the withers; and the ground elec- The comparison showed that the capture system with trode above the left mid-thorax was placed between the textile electrodes was less prone to motion artefacts than remaining two pairs of electrodes. ECG tracing using conventional Ag/AgCl. The studies [19, 83, 84] presented a wearable ECG In addition to external monitors, one can also find monitoring system for the capture of cardiac activity in implanted loggers, which are defined as miniature, ani - horses. The system consisted of an electronic unit, a band mal-borne, electronic devices for logging and/or relaying K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 7 of 21 Measurement systems of data about an animal’s movement, behavior, physiol- This pilot study compares two methods of monitoring ogy and/or environment [98]. The advantage of these cardiac activity: the standard ECG-based system and an loggers is that they can also be used for measurements in alternative method based on ballistocardiography. The small animals, where the placement of external sensors details of the measurement systems are as follows: may be problematic or completely impossible, especially in animals moving in the air or water. In addition, as with external monitors, the sensor does not lose contact with 1. ECG measurement system—adhesive silver/silver the animal’s body. On the other hand, the risks associated chloride (Ag/AgCl) electrodes and a bioamplifier with the invasiveness of the method, such as inflamma - by g.tec medical engineering (Schiedlberg, Austria) tion or encapsulation at the site of implementation, must were used for ECG measurement. The g.USBamp be considered [98]. RESEARCH amplifier is a device of great accuracy One of the companies offering a wide range of differ - for measuring and processing biological signals ent types of implanted loggers is Star-Oddi hf (Garða- (the physiological activity of the eyes, brain, mus- bær, Iceland). Star-Oddi implanted loggers allowing cles, heart, and other organs). The amplifier is sup - to monitor HR derived from a leadless single channel plied with a USB interface and 16 simultaneous A/D ECG, temperature, real-time telemetry or the depth at delta-sigma type converters with 24-bit resolution which the animal is located. Star-Oddi implanted log- and sampling frequency range of 64 Hz–38.4 kHz. ger was used to assess ECG-derived HR in Atlantic The input range of this amplifier is ± 250 mV, which cod in [99], for evaluation of HR and swimming activ- allows direct voltage signals to be recorded without ity as stress indicators for Atlantic salmon in [100], saturation. The amplifier includes an internal unit for or for HR monitoring in large decapod crustaceans calibrating individual input channels and circuits for [101]. In mammals, the leggers were used for example measuring the impedance of individual electrodes. in [102], where the authors monitored cattles. Moreo- The block diagram of attachment for ECG measure - ver, in [103], the loggers were used in domestic sheep ment is shown in Fig. 3. All hardware and software monitoring. used is summarised in Table 4. 2. BCG measurement system—BCG measurement Materials and methods using a mechanical vibration sensor was performed This pilot study focuses on comparison of ECG and BCG simultaneously with ECG measurement. This meas - measurement systems based on sensing electrical and urement system was implemented with the following mechanical activity of the animal’s heart, respectively. devices by National Instruments (Austin, TX, USA): These systems have proved effective in measuring cardiac NI cDAQ-9185, which is a configurable chassis, and activity in humans. This section describes the equipment, the NI-9234 module. A microphone and sensor were attachment of the measurement systems, placement of also used to capture the mechanical vibrations pro- the ECG electrodes and BCG sensor, methods used to duced by the movement of blood inside large vessels. process the measured signals, and the parameters used The sensor was made from a spiral-shaped deform - to assess the accuracy of measurement. Measurement able plastic tube. was performed in four subjects: a goat, a cow, a horse, and a sheep. The subjects were provided by the Clinic of Movements of the animal’s body caused by quicker Ruminant and Pig Diseases and the Clinic of Horse Dis- blood flow vibrated the particles of the acoustically eases of the Veterinary and Pharmaceutical University in enclosed environment inside the spiral. Pressure changes Brno. A summary of the measured subjects is presented were transferred via a plastic tube to the measuring in Table 3. Table 3 Summary of measured subjects Animal Identification Number Breed Age (years) Note Expected HR values (BPM) Goat 09,088/968 White shorthaired goat 1 – 70–100 Cow 254,887/962 Holstein cow 3 Last delivery 21 days 60–80 ago (still-born calf ) Horse Private breeding Hanoverian horse 11 3 week post-abdomi- 30–40 nal surgery Sheep 092,807/961 Crossbreed 1 – 70–90 Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 8 of 21 gUSBamp PC Bandpassfilter Notch filter Sigma-delta 24bit 8th orderButtherworth 4th orderButtherworth LabVIEW ADC Right side Leftside fl =0.5 Hz,fh= 60 Hz fl =48Hz, fh =52Hz . . . RA GND + LA LL . . . REF . . . n= 16 n= 16 n= 16 Bandpassfilter Notch filter Sigma-delta 24bit Localdata 8th orderButtherworth 4th orderButtherworth ADC unit fl =0.5 Hz,fh= 60 Hz fl =48Hz, fh =52Hz Fig. 3 Block diagram for ECG measurement Table 4 Hardware and software used Name HW/SW Manufacturer Model Bioamplifier HW g.tec gUSBamp RESEARCH Configurable chassis HW National instruments cDAQ-9185 Measurement module HW National instruments NI-9234 Microphone HW GRAS 40PP CCP MATLAB SW MathWorks Matlab R2017a LabVIEW SW National instruments 2018 Device driver SW National instruments NI-DAQmx 19.5 Device driver SW National instruments NI-VISA 19.5 Device driver SW g.tec gUSBamp driver 3.16.00 g. Hlsys library SW g.tec 2.14.00 Electrode and bcg sensor placement microphone GRAS 40PP CCP by G.R.A.S. (Holte, Den- For the ECG measurements, the electrodes were posi- mark) and then converted into voltage signals. The tioned and attached according to the Einthoven triangle, microphone has a wide frequency range from 10 Hz to i.e., one electrode was placed on the right front leg, the 20 kHz and sensitivity of 50 mV/Pa. The signals were second on the left front leg, and the third electrode in then digitalised using the NI-9234 module, which is suit- the abdominal area to ensure that the animal’s heart was able for measuring sounds or vibrations from acceler- in the centre of the Einthoven triangle. To improve the ometers or microphones. The module has four channels, electrode adhesion and ECG signal quality, we sheared 24-bit resolution, a sampling frequency of 51.2 kS/s and the locations, where the electrodes were to be placed an input range of ± 5 V. The digitalised signal was sent via with electrical shears and the skin was cleaned with gel– the cDAQ-9185 ethernet chassis to a PC. This ethernet alcohol disinfectant solution septoderm from Schülke & interface is a four-slot compact data acquisition (DAQ) Mayr GmbH (Norderstedt, Germany). To reduce skin system designed for the collection of data or switching resistance and artifacts, these positions were also cleaned slow action members. The chassis has a controller with with abrasive fine sandpaper. The BCG sensor was placed configurable firmware responsible for timing, synchroni - on the left front leg to be as close to the animal’s heart as sation of measurement tasks and data transfer between possible while sufficiently adhering to the animal’s body the I/O modules and the external control unit. The block in view of its size. Details and examples of attachment in diagram of attachment for BCG measurement is shown the case of individual animals are summarised below. in Fig. 4. K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 9 of 21 Microphone GRAS 40PP CCP LabVIEW Leftside Chassis Module NI-9234 cDAQ-9185 Local data unit Fig. 4 Block diagram for BCG measurement The goat was cleaned and sheared thoroughly in loca - tions selected for attachment of the ECG electrodes Right side before commencing the measurement. The positions RA GND according to the Einthoven triangle were used to capture REF ECG signals. The BCG sensor was attached on the left front leg (see Fig. 5). Attachment of the ECG electrodes on the cow was a) b) also according to the Einthoven triangle. The cow was partially immobilised (enclosed in a cattle chute) for the Left side purposes of measurement, subsequently cleaned at the LA selected points and connected to the measurement sys- LL tem via electrodes. The BCG sensor was attached on the left front leg (see Fig. 6). The ECG measurement on the horse was per - formed according to a modified Einthoven triangle. Fig. 5 Example of (a) attachment diagram and (b) actual measurement of a goat Right side GND RA REF a) b) Left side LA LL Fig. 6 Example of (a) attachment diagram and (b) actual measurement of a cow Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 10 of 21 Right side GND REF RA a) b) Left side LA LL Fig. 7 Example of (a) attachment diagram and (b) actual measurement of a horse Right side RA GND REF a) b) Left side LA LL Fig. 8 Example of (a) attachment diagram and (b) actual measurement of a sheep The electrodes were relocated to the abdominal area. and carefully cleaned with gel–alcohol disinfectant The BCG sensor was attached to the left front leg (see solution. While the electrodes were placed according Fig. 7). to the general model, the exact positions of the elec- Finally, the attachment and placement of electrodes trodes are not evident in the image in Fig. 8b because for the ECG measurement system was the most compli- of the animal’s thick coat of wool. The BCG sensor was cated on the sheep due to the presence of lanolin on the attached to the left front leg (see Fig. 8). sheep’s skin, although the selected parts were sheared K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 11 of 21 Fig. 9 Comparison of the noisy and filtered signals with significant peaks highlighted: a noisy ECG signal from the LL lead in the goat and b is the ECG signal after application of the IIR Buttherworth filter, R peaks are marked red c noisy BCG signal from horse, and d the BCG signal filtered by IIR-WT, J peaks are marked red Signal processing ECG processing, was used to detect the J peak, which The first step in processing the ECG signals was filter - corresponds to the R peak in the ECG signal. The BCG ing. Poorly selected filters can affect the resulting sig - signals were also processed using MATLAB software. nals and their analysis significantly. The captured ECG An example of the originally measured (noisy) BCG signals were first filtered with the digital filters included signal from horse and the filtered BCG is provided in with the bioamplifier. MATLAB software by Math - Fig. 9c, d, respectively. Works (Natick, Massachusetts, USA) and the IIR But- terworth filter [104– 108] BPF type were then applied for subsequent processing of the measured signals. In Evaluation metrics our tests, the IIR Buttherworth filter was more effec - To verify the suitability of ECG for the purposes of tive in filtering interference than the FIR filter, which monitoring cardiac activity in animals and whether it altered the shape of the ECG signal, and may potentially can be used as a reference, we calculated and compared cause unnecessary loss of clinically significant informa - three very frequently used parameters and the aver- tion. The ECG signals for each animal and lead were age HR obtained from the individual leads. The stand - processed using a sixth order filter with cutoff frequen - ard deviation of the length of the NN interval (SDNN) cies set at 2 Hz and 40 Hz. This frequency band was is the simplest to calculate, as it is the square root of selected, since most of the ECG signal energy occupies the variance. The SDNN parameter may be inter - this frequency band. It is important to note that this is preted according to the statement that the higher the sufficient for the HR determination based on R peak SDNN parameter, the greater HRV, which also indi- detection, not for precise ECG morphological analy- cates increased adaptability of the autonomic nervous sis. An example of the originally measured ECG signal system. As the SDNN value decreases, the variability is from the LL lead on the goat and filtered signals is pro - less and only limited autonomic regulation is present. vided in Fig. 9a, b. Once the ECG signals were filtered, SDNN is expressed according to R peaks were detected using a detector which applied a continuous wavelet transform (Gaussian mother wave- SDNN = NN − NN , (1) let with a width of one and five levels of decomposition) N − 1 i=1 [109]. The distances between individual R peaks, i.e., RR intervals, were calculated and applied to ascertain where NN indicates the value of the ith NN interval, N the values of the current HR, the average HR, and the is the total number of intervals, and NN is the average HRV parameters. value of the NN intervals. Since the variance mathemati- The BCG signals were processed using a IIR Butter - cally equals the total power of spectral analysis, SDNN worth BPF type third order filter with cutoff frequen - reflects all the cyclic elements responsible for variability cies 5 Hz and 20 Hz in combination with a WT method in the period of recording [20]. In practice, it is not suit- [110–114] which applied the symlet8 wavelet and three able to compare the SDNN obtained from recordings of levels of decomposition. The same detector used for Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 12 of 21 different duration (because this quantity depends on the as a final step; the window was selected for each animal length of the recording period) [20]. (according to its HR) and ranged from 15 to 25 samples. The RMSSD parameter defined as the square root of Finally Bland–Altman plots were used to evaluate the the mean quadratic differences of consecutive NN inter - accuracy of the measured BCG signals in comparison vals can also be used. The RMSSD parameter is used to to the reference ECG signals. These plots are often used estimate the vagally mediated changes, which are also to compare two medical measurements. The average of reflected in HRV. When an animal is stressed, parasym - the measured pairs is recorded on the horizontal axis, pathetic activation is reduced and the RMSSD values are and the difference between these two measurements is thus lower. RMSSD is expressed according to recorded on the vertical axis. A 95% confidence interval is frequently applied to estimate the interval μ ± 1.96σ, where we can expect to find 95% of the difference values RMSSD = (NN − NN ) . (2) i+1 i [115]. Using Bland–Altman plots, the present study com- N − 1 i=1 pares the vectors and HR values obtained from the BCG signals to the HR values obtained from the ECG signals. The AVNN parameter is defined as the average NN interval duration. All these measurements of short-term Results variation estimate the high-frequency variation in the HR To obtain the best possible results, we tested the effect and are, therefore, heavily correlated [20, 37]. of the filtering on the measured signals and assessed The HR traces were also used to assess the precision of them visually. The best results were obtained using the ECG signals measured from individual leads. We also the IIR Buttherworth filter with cutoff frequencies of evaluated whether the values of the average HR matched 2 Hz and 40 Hz (i.e., the range of the useful ECG sig- the mental condition of the animals during the measure- nal). An example of the resulting filtered signals in all ment. To capture this, values of the current HR values the animals tested is given in Fig. 10. The LA lead is had to be derived first. This was achieved using a R peaks shown for the goat, and cow, and the LL lead is shown detector. The intervals between individual R peak posi - for the horse and sheep. The results in the figure show tions were derived and converted to the current HR val- that the signals were filtered suitably and that the ECG ues in BPM according to signal did not deteriorate because of an inappropriately selected filtering technique. Despite some of the ani - HR = · 60 (3) mals being calm during measurement, a small propor- tion of the signals were affected by motion artefacts. where T is the peak-to-peak time difference, and HR is Therefore, some of the R-peaks could not be detected the resulting heart rate. The moving average was applied a) e) b) Time (s) c) f) d) 260 261 262 263264 265 27 28 29 30 31 32 Time (s) Time (s) Fig. 10 Example of filtered clean ECG signals in each of the tested animals and ECG signals affected by motion artifacts: a filtered signal (LA lead, goat), b filtered signal (LA lead, cow), c filtered signal (LL lead, horse), d filtered signal (LL lead, sheep); e with motion artifacts (LA lead, goat); f with motion artifacts (LL lead, sheep) K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 13 of 21 Table 5 Values of the average HR and values of the HRV measured in individual animals determined from the ECG leads (LL, RA, and LA) and BCG signal Animal Channel Average HR (BPM) RMSSD (ms) SDNN (ms) AVNN (ms) Goat ECG-LL 122.87 533.70 152.40 511.47 ECG-RA 121.55 525.10 124.30 510.21 ECG-LA 121.76 531.90 138.40 513.57 BCG 126.46 808.05 208.31 494.52 Cow ECG-LL – – – – ECG-RA 57.89 1072.40 154.30 1061.20 ECG-LA 57.94 1075.60 168.10 1062.41 BCG 57.42 1221.43 203.78 1158.71 Horse ECG-LL 36.51 1650.50 98.00 1647.62 ECG-RA – – – – ECG-LA 36.56 1644.60 99.60 1643.58 BCG 36.66 1748.40 120.84 1707.30 Sheep ECG-LL 135.30 467.80 84.70 460.07 ECG-RA – – – – ECG-LA – – – – BCG – – – – shown in Fig. 10e, f. Example (e) represents part of the signal ECG signal in the goat from the LA lead, exam- a) ple (f ) shows the ECG signal of the sheep from the LL lead. Figure. 11 shows the examples of the BCG signals after filtration. We can see that the visual quality of the b) signals is lower than in case of ECG measurement. To compare the quality of those methods objectively, we used Bland–Altman analysis (Table 6 and Fig. 14) and investigated the differences between the obtained HRV c) traces (Figs. 12 and 13). Table 5 summarizes the obtained values of the aver- 96 97 98 99 100 101 age HR and values of the HRV measured in individual Time( s) animals. Higher values of average HR were expected Fig. 11 Examples of filtered BCG signals in each of the tested in the case of the goat and sheep, since both of these animals: a goat, b cow, c horse animals were stressed and trembling during measure- ment. Measurement in the cow and horse proceeded without problems, since the cow and horse were calm Table 6 Mean values d and values of ±1.96s measured from during measurement. The values of the average HR BCG signals were, therefore, expected to fall within the physiologi- Measurement system Animal ± 1.96 s cal range. (BPM) (BPM) The signals from all leads (LL, RA, LA) during ECG measurement in the goat were captured in high quality BCG Goat 3.63 51.56 and could, therefore, be used for further analysis along Cow − 0.53 6.91 with the BCG signal. As shown by results in Table 5, there Horse − 0.24 2.35 were similar average values of HR and the HRV param- eters obtained from individual ECG leads with a negligi- ble difference between individual leads in the evaluation and this caused minor differences between individual metrics. The average HR for the LL lead was 122.87 BPM, leads in the analysis (see Table 5). The effect of the the average HR for the RA lead was 121.55 BPM, and the animals’ movements on the quality of ECG capture is Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 14 of 21 BCG BCG BCG ECG ECG ECG a) b) c) 25 26 27 28 29 30 237238 239240 241242 383384 385386 387388 Time (s) Time (s) Time (s) BCG ECG 0 100 200300 400500 600 Time (s) BCG BCG BCG ECG ECG ECG d) e) f) 106 107 108109 110111 340341 342 343 344345 552553 554555 556557 Time (s) Time (s) Time (s) Fig. 12 Illustration of the effect of the quality of recording on the HR traces (horse). Examples a, b and c correspond to the sections, where high accuracy was achieved in determining the HR; examples d, e and f correspond to the sections, where determining the HR was less accurate highest HR for the RA lead was 122.87 BPM. In case of and 59.60 ms, respectively. In case of BCG channel, an BCG channel, an average HR of 126.46 BPM was calcu- average HR of 36.66 BPM was calculated from the BCG lated from the BCG signal in goat. signal. Only two ECG signals (RA, LA leads) measured in the Measurement of the sheep was the most difficult due to cow were used for further analysis along with the BCG significant restlessness in the animal. Only one captured signal, since the data measured from the third lead was signal (from the LL lead) was useful for further analysis. not of suitable quality and thus could not be used. An The average HR measured with the LL lead was 135.30 analysis was, therefore, performed on the data obtained BPM. from the leads on the right and left sides (RA and LA, To obtain accurate information about the HR, the sig- respectively). The values of the parameters obtained from nals must be of sufficient quality so that the significant the signals in both leads showed only slight deviation in peaks can be detected. This is illustrated in Figs. 12 and the evaluation metrics. The average HR was 57.89 BPM 13 that show examples of HR traces determined using in the RA lead and 57.94 BPM in the LA lead. An average both ECG and BCG signals in horse and cow. There are HR of 57.42 BPM was calculated from the BCG signal in parts, where the HR traces overlap (i.e., both methods the cow. determined the same HR) and also parts, where they dif- Signals from the LA and LL leads were also captured fer. In both figures, the examples (a), (b) and (c) corre - from the horse. The values of the HR and HRV param - spond to the sections, where determining the HR showed eters obtained from both leads were similar, with a neg- a high level of accuracy. These are well captured BCG sig - ligible difference in in evaluation metrics. The average nals. In contrast, examples (d), (e) and (f ) correspond to HR was 36.51 BPM in the LL lead and 36.56 BPM in the the sections, where the HR trace in BCG deviated from LA lead, while the SDNNs for these leads were 98.00 ms, HeartRate(bpm) K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 15 of 21 BCG BCG BCG ECG ECG ECG a) b) c) 94 95 96 97 98 99 281282 283284 285 286 594 595 596597 598599 Time (s) Time (s) Time (s) BCG ECG 0100 200300 400 500 600 Time (s) BCG BCG BCG ECG ECG ECG d)e)f) 235236 237 238239 240 490 491 492493 494 495 119120 121 122 123124 Time (s) Time (s) Time (s) Fig. 13 Illustration of the effect of the quality of recording on the resulting HR traces (cow). Examples a, b and c correspond to the sections, where high accuracy was achieved in determining the HR; examples d, e and f correspond to the section, where determining the HR was less accurate 120 120 80 80 40 40 0 0 0 -40 -40 -40 -80 -80 -80 90 110 130 150 170 190 30 35 40 50 60 70 80 (BCG+ ECG)/2 (bpm) (BCG+ ECG)/2(bpm) (BCG+ ECG)/2 (bpm) (a)(b) (c) Fig. 14 Comparison based on the Bland–Altman plots of the reference values and the estimated BCG values in HR measurements of a the goat, b the cow and c the horse. The middle horizontal line indicates the mean d of all differences. The upper and lower horizontal dashed lines indicate 95% limits of agreement that lie in the interval d ± 1.96 s (BCG -ECG)(bpm) HeartRate(bpm) (BCG -ECG)(bpm) (BCG -ECG)(bpm) Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 16 of 21 the reference ECG trace. This deviation resulted from 36.51 BPM and 36.56 BPM in the LL and LA ECG lead, interference which reduced the quality of the signal. respectively. This reflects the physiological values, which Finally, to objectively compare the quality of the sig- fall in the range of 30–40 BPM with an average value of nals acquired by the ECG and BCG methods, Bland– 35 BPM [117]. The values confirm the assumption that Altman analysis was used, see Table 6 and Fig. 14. We the animal was not stressed. can interpret the data in a way that the less the range of However, the situation in the goat and sheep was dif- the confidence interval, the less the difference between ferent, which is evident in HR values and also on the sig- the HR from the BCG signals and the HR from the ref- nal quality. The average HR in goat for the LL, LA, and erence ECG. The values should ideally be near the bias RA ECG lead was 122.87 BPM, 121.55 BPM, and 122.87 horizontal line, which should be close to zero, and BPM, respectively. These values were slightly higher than show a mean d for all differences. The mean values d the physiological values, which were expected within the and values of ±1.96s are summarised in Table 6. The range of 70–100 BPM with an average value of 90 BPM BCG signals could only be captured in the goat, cow, [116]. The average HR values, therefore, confirm the and horse, while the attempt in the sheep was not suc- hypothesis that the goat was stressed, and the ECG meas- cessful. According to the results presented in Table 6 urement can be considered precise. These results (high and because high values of d and ±1.96s were deter- HR values) are also consistent with our observations that mined, we can state that the BCG method was not the goat appeared unsettled during HR recordings. effective in the goat. The BCG method in the cow and The sheep was the most problematic subject in ECG horse was effective, because low values of d and values measurement mainly because of the animal’s thick coat of ±1.96s were achieved in each case. Figure 14a shows of wool and lanolin layer on the skin. In this case, the the Bland–Altman plots for signals captured in the electrodes could not be attached securely on the animal’s goat, where measurement with BCG failed entirely, and body despite shaving and addressing the skin with alco- examples (b) and (c) present the signals measured in hol solution. Only one of the ECG signals was, therefore, cow and horse, respectively, where the measurements used for analysis. The average HR measured with the LL were effective. lead was 135.30 BPM. These is an extremely high value compared to the physiological values, which fall in the Discussion range of 70–90 BPM [116]. It is, therefore, evident, simi- The present study examined the usability of alternative larly as in the case of the results in goat, that the animal measurement systems in monitoring the cardiac activ- was highly stressed and unsettled during the measure- ity of animals. The accuracy of the ballistocardiography ment procedure. method was compared to the verified standard, i.e., the The problem regarding the measurements was that the ECG measurement system. Herein, this assumption animals were not used to this (or any) kind of monitor- was verified for the used ECG measurement system by ing and thus the preparations, especially in case of ECG, comparing the values obtained from individual leads were stressful for them. The ECG measurement included (Table 5). Measurement of the cardiac activity in animals shaving in several parts of their body, application of alco- using ECG was considered representative and was, thus, hol to clear these areas and also addressing the conduc- used as a reference or ground truth. During the meas- tivity between the electrode and the skin by means of urements, we encountered several problems and were sandpaper. This procedure of shaving fur, attaching the unable to satisfactorily measure all the signals in some electrodes and measuring ECG was particularly demand- animals. This section discusses the possible reasons for ing in goat and sheep, which was also reflected in the these problems and offers practical insights into each measurements (higher HR values, motion artefacts). method. Therefore, the future research should include compari - Measurement in the cow and horse proceeded without son of the measurements when it is used along with the problems, since the cow and horse were not unsettled ECG and alone. We believe that when the BCG sensor during measurement. The values of the average HR were, is incorporated in a saddle or a belt and thus placed on therefore, expected to fall within the physiological range. the animal quickly and easily, the HR will be lower (i.e., As for the measurement in cow, the average HR was 57.89 the animal will be less stressed) than when the process BPM and 57.94 BPM in the RA and LA lead, respectively, includes preparations for the ECG based monitoring. indicating slightly lower values than the physiological val- One of the advantages of the BCG sensor was quick ues in cows, which fall in the range of 60–80 BPM with an and simple attachment to the animal without the need average value of 70 BPM [116]. The results confirmed the for shaving. As this is an alternative method for the hypothesis that the cow was calm during measurement. measurement of cardiac activity, no standard guidelines In case of the measurement in horse, the average HR was or recommendations are available for sensor placement. K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 17 of 21 The BCG sensor was placed on the left front leg to be as the useful signal. These adaptive algorithms have proved close as possible to the animal’s heart and for the best useful in the past in processing human biological signals, adherence to the animal’s body according to its size. such as BCG and ECG in adults [118–120], fetal ECG However, the size of the sensor contributed to poor adhe- [121–123], speech signals [124, 125], or signals used in sion to the body of the animal and motion artefacts in telecommunications [126]. the captured signal due to movement of the sensor with The pilot study introduced research in the given area, small animals. During measurement of BCG signal in the and future research will focus on measuring the car- goat, the sensor moved frequently because of the animal’s diac activity in cattle and horses. Measurement of BCG restlessness, resulting in motion artefacts and significant in these animals is shown to be precise, and the benefits distortions to the signal. A high-quality BCG signal could may be economically significant (e.g., in milk produc - not be obtained from the goat even after filtering, and tion or racehorse training). In future, additional sensor the detector was unable to detect the J peaks correctly. types and measurement methods to obtain HR at rest The BCG signal could not be captured at all in the sheep or in motion will be tested. This has an important con - because of the wool covering the animal’s body. tribution, for example, in horse training. Advanced fil - However, sufficient contact between the sensors and tering methods (adaptive algorithms) will also be tested the animal’s body was achieved with the larger ani- to measure the cardiac activity of animals under load. In mals, i.e., the cow and horse. The BCG signals were only the case of horses, measurement of HR recovery, which is affected slightly by motion artefacts, and high-quality defined as the reduction in the HR 1 min after training, is signals were captured. This was confirmed by the results a major indicator of the horse’s form. obtained from the Bland–Altman plots, where in horse Future research will also focus on determining a gold and cow, the results showed agreement between the two standard for the placement of sensors and electrodes in methods. On the other hand, in case of the poor BCG individual animal species and thereby facilitate the best measurements in goat, the results of Bland–Altman possible quality in future records. This could also lead to analysis show lack of agreement with the ECG reference. the creation of a measurement system for use in small The findings are also reflected in the HR analysis of the farms up to large breeding operations to monitor the animals on both data, especially on the average HRs. The health and well-being of animals. Creating optimal con- average of 36.66 BPM was calculated from the BCG sig- ditions for the life of animals or the early detection of nal in the horse, which matched the average HR deter- disease may help reduce economic loss and increase pro- mined from the ECG signals (36.51 and 36.56 BPM). An ductivity in farms. The use of HRV monitoring during the average HR of 57.42 BPM calculated from the BCG sig- training of race animals is a separate topic which is cur- nal in the cow matched the average HR determined from rently enjoying attention. This method may help optimise the ECG signals (57.89 BPM and 57.94 BPM). Contrary, the training, recuperation and physical readiness of ani- in case of the goat, the obtained HR average was 126.46 mals and thus improve their form and performance. BPM, which is higher than the averages of the HRs obtained using the ECG leads (122.87 BPM, 121.55 BPM, Conclusions and 121.76 BPM). The present study focused on the measurement of car - Obtaining a high-quality signal to monitor the car- diac activity in animals using ECG and BCG systems in diac activity of animals by means of BCG method thus four animals: a goat, a cow, a horse, and a sheep. First, depends on the selection of suitable sensor size (corre- the suitability of ECG was verified by comparing the cal - sponding to the animal’s size), placement of sensors and culated evaluation parameters (the average HR, RMSSD, sufficient contact with the animal’s skin. We, therefore, SDNN and AVNN) and comparing the HR traces from recommend integrating sensors into a sensor belt or individual leads. The relation between the average HR a saddle (for horses) in future research. This monitor - values and the observed mental state and behaviour of ing method may provide sufficient contact to minimise the animals during measurement was also discussed. the movement of the sensors. In addition to the sensors Because ECG was considered a valid baseline for compar- which capture cardiac activity, a reference sensor can be ison, it was used in this study as a reference for assessing placed elsewhere on the animal to capture the signals the accuracy of the BCG method. The captured signals with interference from motion, while the animal is mov- were analysed by comparing the accuracy of the current ing (in particular horses under load). The signal which HR values using Bland–Altman plots. Measurements includes the horse’s cardiac activity and motion artefacts of the BCG signals were accurate in the goat, cow, and and the signal which contains only motion signals from horse. BCG signals could not be measured in the sheep, the reference sensor could be used as inputs for an adap- since BCG sensor was highly prone to poor adhesion to tive algorithm which can suppress the interference in the animal’s body and movement, while the animal was Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 18 of 21 Competing interests restless, which resulted in motion artefacts. The BCG The authors declare that they have no competing interests. sensor was only shown to be effective with the large ani - mals, i.e., the horse and cow, where sufficient contact Author details Department of Cybernetics and Biomedical Engineering, VSB—Technical Uni- between the sensor and the animal’s body was achieved versity of Ostrava, Ostrava, Czechia. Faculty of Veterinary Medicine, Ruminant owing to their size. and Swine Clinic, University of Veterinary Sciences Brno, Brno, Czechia. In the future research, the placement of sensors should Received: 2 August 2021 Accepted: 3 April 2022 be optimised for individual species to allow capture of the highest possible quality of signal. The measurement system could be embedded into a tailor-made saddle or belt so that the contact of the sensor is ensured, and the References time required for the attachment is minimized. Further 1. Buchanan JW. The history of veterinary cardiology. J Vet Cardiol. tests should also focus on testing additional sensor types 2013;15(1):65–85. https:// doi. org/ 10. 1016/j. jvc. 2012. 12. 002. 2. Detweiler DK. Comparative cardiology and cardiovascular disease. J Clin and measurements with animals which are at rest but Epidemiol. 1962;15(9):867–78. https:// doi. org/ 10. 1016/ 0021- 9681(62) also in motion. Finally, advanced signal processing meth- 90056-5. ods will play a crucial role to eliminate the motion arti- 3. Detweiler DK, Patterson DF. The prevalence and types of cardiovascular disease in dogs. Ann N Y Acad Sci. 1965;127(1):481–516. https:// doi. org/ facts more efficiently. 10. 1111/j. 1749- 6632. 1965. tb494 21.x. 4. Detweiler DK. Generic aspects of cardiovascular diseases in animals. Circulation. 1964;30:114–27. https:// doi. org/ 10. 1161/ 01. cir. 30.1. 114. Abbreviations 5. Patterson DF, Detweiler DK, Glendenning SA. Heart sounds and HR: Heart rate; HR : Maximum heart rate; HR : Minimum heart rate; HRV: max min murmurs of the normal horse*. Ann N Y Acad Sci. 1965;127(1):242–305. Heart rate variability; ECG: Electrocardiography; BCG: Ballistocardiography; https:// doi. org/ 10. 1111/j. 1749- 6632. 1965. tb494 07.x. PCG: Phonocardiography; PPG: Photoplethysmography; RMSSD: Root mean 6. Detweiler DK. The Mammalian Electrocardiogram: Comparative Fea- square of successive RR interval differences; SDNN: Standard deviation of tures. In: Macfarlane PW, van Oosterom A, Pahlm O, Kligfield P, Janse M, normal-to-normal interval duration; AVNN: Average normal-to-normal interval Camm J, editors. Comprehensive Electrocardiology. London: Springer; duration; SDANN: Standard deviation of the average normal-to-normal inter- 2010. https:// doi. org/ 10. 1007/ 978-1- 84882- 046-3_ 42. vals; pNN50: Percentage of successive NN intervals; SEM: Standard error of the 7. Detweiler DK, Luginbuhl H, Buchanan JW, Patterson DF. The natural mean NN interval; LL, RA, LA: Left leg, right arm, left arm ECG leads; IIR: Infinite history of acquired cardiac disability of the dog*. Ann N Y Acad Sci. impulse response; FIR: Finite impulse response; WT: Wavelet transform.; DAQ: 1968;147(8):318–29. https:// doi. org/ 10. 1111/j. 1749- 6632. 1968. tb455 Compact data acquisition. 65.x. 8. Manuja B, Manuja A, Aich P. Stress and its impact on farm animals. Front Acknowledgements Biosci. 2012;E4:1759–67. Not applicable. 9. Gebregeziabhea E. The effect of stress on productivity of animals: a review. J Biol Agric. 2015;5(3):165. Author contributions 10. Carvalho ER, Franco GG. Heart rate variability as a non-invasive method RM created the concept and supervised the project. RK designed the concept to assess welfare in dairy cows. Approaches in poultry. Behav Sci. for the measurement setup, sensor placement, and signal processing. JB 2018;3(1):1–3. https:// doi. org/ 10. 31031/ APDV. 2018. 03. 000553. created the new software tool for measurement and analysis of the animal 11. Bennet L, Gunn AJ. The fetal heart rate response to hypoxia: insights data. JK designed and implemented the hardware tools of the alternative from animal models. Clin Perinatol. 2009;36(3):655–72. https:// doi. org/ pneumatic sensors. RK, JB, JK, and IS carried out the data acquisition. RK, JB, 10. 1016/j. clp. 2009. 06. 009. and KB analysed and interpreted the cardiac data. KB, RK, IS, and JK contrib- 12. Turbill C, Ruf T, Mang T, Arnold W. Regulation of heart rate and rumen uted in writing and revising the manuscript. All authors read and approved temperature in red deer: effects of season and food intake. J Exp Biol. the final manuscript. 2011;214(6):963–70. https:// doi. org/ 10. 1242/ jeb. 052282. 13. Hopster H, Blokhuis HJ. Validation of a heart-rate monitor for measuring Funding a stress response in dairy cows. Can J Anim Sci. 1994;74(3):465–74. This work was supported in part by the Ministry of Education of the Czech https:// doi. org/ 10. 4141/ cjas94- 066. Republic under Project SP2021/32, in part by the European Regional Develop- 14. Caroprese M, Albenzio M, Marzano A, Schena L, Annicchiarico G, Sevi ment Fund in the Research Centre of Advanced Mechatronic Systems Project A. Relationship between cortisol response to stress and behavior, within the Operational Programme Research, Development and Education immune profile, and production performance of dairy ewes. J Dairy Sci. under Project CZ.02.1.01/0.0/0.0/16-019/0000867. 2010;93(6):2395–403. https:// doi. org/ 10. 3168/ jds. 2009- 2604. 15. Rushen, J, de Passille AM, von Keyserlingk MAG, Weary DM. Stress and Availability of data and materials physiological indicators of animal welfare. In: Welfare H. Animal Welfare. The data sets used and/or analysed during the current study are available Dordrecht: Springer Netherlands; 2008. p. 43–69. https:// doi. org/ 10. from the corresponding author on reasonable request. 1007/ 978-1- 4020- 6558- 33. 16. Boissy A, Le Neindre P. Behavioral, cardiac and cortisol responses to brief peer separation and reunion in cattle. Physiol Behav. 1997;61(5):693–9. Declarations https:// doi. org/ 10. 1016/ s0031- 9384(96) 00521-5. 17. Kovács L, Jurkovich V, Bakony M, Szenci O, Póti P, Tozser J. Welfare Ethics approval and consent to participate implication of measuring heart rate and heart rate variability in dairy The data were collected in clinical conditions as part of project VFU-9-2019 cattle: literature review and conclusions for future research. Animal. approved by the competent University Bioethics Committee at the Faculty of 2014;8(2):316–30. https:// doi. org/ 10. 1017/ S1751 73111 30021 40. Veterinary Medicine, Ruminant and Swine Clinic, University of Veterinary and 18. Kuwahara M, Hashimoto S, Ishii K, Yagi Y, Hada T, Hiraga A, et al. Assess- Pharmaceutical Sciences Brno. ment of autonomic nervous function by power spectral analysis of heart rate variability in the horse. Auton Neurosci. 1996;60(1–2):43–8. Consent for publication https:// doi. org/ 10. 1016/ 0165- 1838(96) 00028-8. Not applicable. K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 19 of 21 19. Guidi A, Lanata A, Baragli P, Valenza G, Scilingo EP. A wearable system 40. Perkins JD, Bowen IM, Else RW, Marr CM, Mayhew IG. Functional and for the evaluation of the human horse interaction: a preliminary study. histopathological evidence of cardiac parasympathetic dysautonomia Electronics. 2016;5(4):63. https:// doi. org/ 10. 3390/ elect ronic s5040 063. in equine grass sickness. Vet Rec. 2000;146(9):246–50. https:// doi. org/ 20. Malik M. Heart rate variability. Ann Noninvasive Electrocardiol. 10. 1136/ vr. 146.9. 246. 1996;1(2):151–81. https:// doi. org/ 10. 1111/j. 1542- 474X. 1996. tb002 75.x. 41. Rietmann TR, Stauffacher M, Bernasconi P, Auer JA, Weishaupt MA. The 21. Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and association between heart rate, heart rate variability, endocrine and norms. Front Public Health. 2017;5:258. behavioural pain measures in horses suffering from laminitis. J Vet Med 22. Von Borell E, Langbein J, Després G, Hansen S, Leterrier C, Marchant- A Physiol Pathol Clin Med. 2004;51(5):218–25. https:// doi. org/ 10. 1111/j. Forde J, et al. Heart rate variability as a measure of autonomic 1439- 0442. 2004. 00627.x. regulation of cardiac activity for assessing stress and welfare in farm 42. Kuwahara M, Hiraga A, Nishimura T, Tsubone H, Sugano S. Power spec- animals—A review. Physiol Behav. 2007;92(3):293–316. https:// doi. org/ tral analysis of heart rate variability in a horse with atrial fibrillation. J Vet 10. 1016/j. physb eh. 2007. 01. 007. Med Sci. 1998;60(1):111–4. https:// doi. org/ 10. 1292/ jvms. 60. 111. 23. Yao Y, Shin S, Mousavi A, Kim CS, Xu L, Mukkamala R, et al. Unobtrusive 43. McConachie EL, Giguere S, Rapoport G, Barton MH. Heart rate variability estimation of cardiovascular parameters with limb ballistocardiography. in horses with acute gastrointestinal disease requiring exploratory Sensors. 2019;19(13):2922. https:// doi. org/ 10. 3390/ s1913 2922. laparotomy. J Vet Emerg Crit Care. 2016;26(2):269–80. https:// doi. org/ 24. Rushen J, Munksgaard L, Marnet PG, DePassillé AM. Human contact 10. 1111/ vec. 12362. and the effects of acute stress on cows at milking. Appl Anim Behav Sci. 44. Mésangeau D, Laude D, Elghozi JL. Early detection of cardiovascular 2001;73(1):1–14. https:// doi. org/ 10. 1016/ s0168- 1591(01) 00105-8. autonomic neuropathy in diabetic pigs using blood pressure and heart 25. Rushen J, De Passillé AM, Munksgaard L. Fear of people by cows and rate variability. Cardiovasc Res. 2000;45(4):889–99. https:// doi. org/ 10. effects on milk yield, behavior, and heart rate at milking. J Dairy Sci. 1016/ s0008- 6363(99) 00406-x. 1999;82(4):720–7. https:// doi. org/ 10. 3168/ jds. S0022- 0302(99) 75289-6. 45. Voss LJ, Bolton DPG, Galland BC, Taylor BJ. Endotoxin effects on markers 26. Joksimovic-Todorovic M, Davidovic V, Hristov S, Stankovic B. Eec ff t of of autonomic nervous system function in the piglet: implications for heat stress on milk production in dairy cows. Biotechnol Anim Husb. SIDS. Neonatology. 2004;86(1):39–47. https:// doi. org/ 10. 1159/ 00007 2011;27(3):1017–23. https:// doi. org/ 10. 2298/ BAH11 03017J. 7452. 27. Gygax L, Neuffer I, Kaufmann C, Hauser R, Wechsler B. Restlessness 46. Khan MS, Zhao N, Sica AL, Gootman N, Gootman PM. Changes in R-R behaviour, heart rate and heart-rate variability of dairy cows milked and Q-T intervals following cardiac vagotomy in neonatal swine. Exp in two types of automatic milking systems and auto-tandem milking Biol Med. 2001;226(1):32–6. https:// doi. org/ 10. 1177/ 15353 70201 22600 parlours. Appl Anim Behav Sci. 2008;109(2):167–79. https:// doi. org/ 10. 105. 1016/j. appla nim. 2007. 03. 010. 47. Janczarek I, Kedzierski W, Stachurska A, Wilk I, Kolstrung R, Strzelec K. 28. Chamove AS, Crawley-Hartrick OJE, Stafford KJ. Horse reactions to Autonomic nervous system activity in purebred Arabian horses evalu- human attitudes and behavior. Anthrozoos. 2002;15(4):323–31. https:// ated according to the low frequency and high frequency spectrum doi. org/ 10. 2752/ 08927 93027 86992 423. versus racing performance. Acta Vet Brno. 2016;85(4):355–62. https:// 29. Lesimple C, Fureix C, Menguy H, Hausberger M. Human direct actions doi. org/ 10. 2754/ avb20 16850 40355. may alter animal welfare, a study on horses (Equus caballus). PLoS ONE. 48. Voss B, Mohr E, Krzywanek H. Eec ff ts of aqua-treadmill exercise on 2010;5(4): e10257. https:// doi. org/ 10. 1371/ journ al. pone. 00102 57. selected blood parameters and on heart-rate variability of horses. J Vet 30. von Konig Borstel U, Euent S, Graf P, Konig S, Gauly M. Equine behaviour Med A Physiol Pathol Clin Med. 2002;49(3):137–43. https:// doi. org/ 10. and heart rate in temperament tests with or without rider or handler. 1046/j. 1439- 0442. 2002. 00420.x. Physiol Behav. 2011;104(3):454–63. https:// doi. org/ 10. 1016/j. physb eh. 49. Munsters CCBM, Visser EK, van den Broek J, van Sloet Oldruitenborgh- 2011. 05. 010. Oosterbaan MM. Physiological and behavioral responses of horses 31. Bartolomé E, Cockram MS. Potential effects of stress on the perfor - during police training. Animal. 2013;7(5):822–7. https:// doi. org/ 10. 1017/ mance of sport horses. J Equine Vet Sci. 2016;40:84–93. https:// doi. org/ S1751 73111 20023 27. 10. 1016/j. jevs. 2016. 01. 016. 50. Deprés G, Veissier I, Boissy A. Eec ff t of autonomic blockers on heart 32. Vetter S, Vasa L, Ózsvári L. Economic aspects of animal welfare. Acta period variability in calves: evaluation of the sympathovagal balance. Polytech Hung. 2014;11(7):119–34. Physiol Res. 2002;51(4):347–53. 33. McBride SD, Mills DS. Psychological factors affecting equine per - 51. Parker M, Goodwin D, Eager RA, Redhead ES, Marlin DJ. Comparison formance. BMC Vet Res. 2012;8(1):180. https:// doi. org/ 10. 1186/ of Polar heart rate interval data with simultaneously recorded ECG 1746- 6148-8- 180. signals in horses. Comp Exerc Physiol. 2009;6(4):137–42. https:// doi. org/ 34. Henningsen A, Czekaj TG, Forkman B, Lund M, Nielsen AS. The 10. 1017/ S1755 25401 00000 24. relationship between animal welfare and economic performance at 52. Behar JA, Rosenberg AA, Weiser-Bitoun I, Shemla O, Alexandrovich A, farm level: a quantitative study of Danish pig producers. J Agric Econ. Konyukhov E, et al. PhysioZoo: a novel open access platform for heart 2018;69(1):142–62. https:// doi. org/ 10. 1111/ 1477- 9552. 12228. rate variability analysis of mammalian electrocardiographic data. Front 35. Sinclair M, Fryer C, Phillips CJC. The benefits of improving animal wel- Physiol. 2018. https:// doi. org/ 10. 3389/ fphys. 2018. 01390. fare from the perspective of livestock stakeholders across Asia. Animals. 53. Vörös K, Nolte I, Hungerbuhler S, Reiczigel J, Ehlers JP, Tater G, et al. 2019;9(4):123. https:// doi. org/ 10. 3390/ ani90 40123. Sound recording and digital phonocardiography of cardiac murmurs in 36. Minero M, Canali E. Welfare issues of horses: an overview and practical dogs by using a sensor-based electronic stethoscope. Acta Vet Hung. recommendations. Ital J Anim Sci. 2009;8(sup1):219–30. https:// doi. org/ 2011;59(1):23–35. https:// doi. org/ 10. 1556/ AVet. 59. 2011.1.3. 10. 4081/ ijas. 2009. s1. 219. 54. Brown CM, Holmes JR. Phonocardiography in the horse: 1. The intracar- 37. Kovács L, Kézér FL, Jurkovich V, Kulcsár-Huszenicza M, Tőzsér J. Heart diac phonocardiogram. Equine Vet J. 1979;11(1):11–8. https:// doi. org/ rate variability as an indicator of chronic stress caused by lameness in 10. 1111/j. 2042- 3306. 1979. tb012 85.x. dairy cows. PLoS ONE. 2015;10(8): e0134792. https:// doi. org/ 10. 1371/ 55. Brown CM, Holmes JR. Phonocardiography in the horse: 2. The relation- journ al. pone. 01347 92. ship of the external phonocardiogram to intracardiac pressure and 38. Stubsjøen SM, Knappe-Poindecker M, Langbein J, Fjeldaas T, Bohlin J. sound. Equine Vet J. 1979;11(3):183–6. https:// doi. org/ 10. 1111/j. 2042- Assessment of chronic stress in sheep (part II): exploring heart rate vari-3306. 1979. tb013 37.x. ability as a non-invasive measure to evaluate cardiac regulation. Small 56. Gargiulo GD, Shephard RW, Tapson J, McEwan AL, Bifulco P, Cesarelli Rumin Res. 2015;133:30–5. https:// doi. org/ 10. 1016/j. small rumres. 2015. M, et al. Pregnancy detection and monitoring in cattle via combined 10. 026. foetus electrocardiogram and phonocardiogram signal processing. 39. Nagel C, Melchert M, Aurich C, Aurich J. Differences in endocrine and BMC Vet Res. 2012;8(1):164. https:// doi. org/ 10. 1186/ 1746- 6148-8- 164. cardiac changes in mares and her fetus before, during, and after parturi- 57. Wijshoff R, Sar TVd, Aarts R, Woerlee P, Noordergraaf G. Potential of tion in horses of different size. Animals. 2020;10(9):1577. https:// doi. org/ photoplethysmography to guide pulse checks during cardiopulmonary 10. 3390/ ani10 091577. Kahankova et al. Animal Biotelemetry (2022) 10:15 Page 20 of 21 resuscitation: observations in an animal study. Resuscitation. 2013;84:1. 77. Heise D, Skubic M. Monitoring pulse and respiration with a non-invasive https:// doi. org/ 10. 1016/j. resus citat ion. 2013. 08. 019. hydraulic bed sensor. In: 2010 Annu Int Conf IEEE Eng Med Biol Soc; 58. Borchevkin D. Method of photoplethysmography diagnostics of 2010. p. 2119–23. ISSN: 1558–4615. https:// doi. org/ 10. 1109/ IEMBS. 2010. domesticated animals cardiovascular diseases. J Ve Sci Technol. 2015. 56272 19. https:// doi. org/ 10. 4172/ 2157- 7579. 10002 87. 78. Lydon K, Su BY, Rosales L, Enayati M, Ho KC, Rantz M, et al. Robust heart- 59. Cugmas B, Struc E, Spigulis J. Photoplethysmography in dogs and cats: beat detection from inhome ballistocardiogram signals of older adults a selection of alternative measurement sites for a pet monitor. Physiol using a bed sensor. In: 2015 Annu Int Conf IEEE Eng Med Biol Soc; 2015. Meas. 2019;40(1):01NT02. https:// doi. org/ 10. 1088/ 1361- 6579/ aaf433. p. 7175–9. ISSN: 1558–4615. https:// doi. org/ 10. 1109/ EMBC. 2015. 73200 60. Nie L, Berckmans D, Wang C, Li B. Is continuous heart rate monitoring 47. of livestock a dream or is it realistic? A review. Sensors. 2020;20(8):2291. 79. Chen Z, Teo JT, Ng SH, Yang X. Portable fiber optic ballistocardiogram https:// doi. org/ 10. 3390/ s2008 2291. sensor for home use. In: Proceedings Volume 8218, Optical Fibers 61. Cui Y, Zhang M, Li J, Luo H, Zhang X, Fu Z. WSMS: wearable stress and Sensors for Medical Diagnostics and Treatment Applications monitoring system based on IoT multi-sensor platform for living sheep XII; 82180X, San Francisco, California, United States. pp. 82180X-1– transportation. Electronics. 2019;8(4):441. https:// doi. org/ 10. 3390/ elect 82180X-7. https:// doi. org/ 10. 1117/ 12. 909768. ronic s8040 441. 80. Deepu CJ, Chen Z, Teo JT, Ng SH, Yang X, Lian Y. A smart cushion for 62. Theodor M, Ruh D, Subramanian S, Förster K, Heilmann C, Beyersdorf F, real-time heart rate monitoring. In: IEEE Trans Biomed Circuits Syst; Plachta D, Manoli Y, Zappe H, Seifert A. Implantable pulse oximetry on 2012. p. 53–6. ISSN: 2163–4025. https:// doi. org/ 10. 1109/ BioCAS. 2012. subcutaneous tissue. In: 2014 36th annual international conference of 64185 12. the ieee engineering in medicine and biology society. pp. 2089–2092; 81. Fung P, Dumont G, Ries C, Mott C, Ansermino M. Continuous noninva- 2014. sive blood pressure measurement by pulse transit time. In: 2004 Annu 63. Pretorius PJ, Terblanche M, van der Walt JJ. Ballistocardiography in some Int Conf IEEE Eng Med Biol Soc. vol. 1; 2004. p. 738–41. https:// doi. org/ domestic animals. Bibl Cardiol. 1968;21:46–52.10. 1109/ IEMBS. 2004. 14032 64 64. Cossio P, Berreta JA, Mosso HE, Perianes I. Ballistocardiogram in experi- 82. Lenoir A, Trachsel DS, Younes M, Barrey E, Robert C. Agreement mental myocardial lesions. Cardiology. 1954;24(6):372–7. https:// doi. between electrocardiogram and heart rate meter is low for the meas- org/ 10. 1159/ 00016 5432. urement of heart rate variability during exercise in young endurance 65. Chakrabarti T, Saha S, Roy S, Chel I. Phonocardiogram signal analysis— horses. Front Vet Sci. 2017. https:// doi. org/ 10. 3389/ fvets. 2017. 00170. practices, trends and challenges: a critical review. 2015 International 83. Lanata A, Guidi A, Baragli P, Valenza G, Scilingo EP. A novel algorithm Conference and Workshop on Computing and Communication (IEM- for movement artifact removal in ECG signals acquired from wearable CON). 2015. https:// doi. org/ 10. 1109/ IEMCON. 2015. 73444 26. systems applied to horses. PLoS ONE. 2015;10(10): e0140783. https:// 66. Anumukonda M, Chowdhury SR. Heart sound sensing through MEMS doi. org/ 10. 1371/ journ al. pone. 01407 83. microphone. In: Postolache OA, Mukhopadhyay SC, Jayasundera KP, 84. Lanata A, Guidi A, Baragli P, Paradiso R, Valenza G, Scilingo EP. Removing Swain AK, editors. Sensors for Everyday Life: Healthcare Settings. Smart movement artifacts from equine ECG recordings acquired with textile Sensors Measurement and Instrumentation. Cham: Springer; 2017. p. electrodes. In: 2015 Annu Int Conf IEEE Eng Med Biol Soc 2015. p. 121–34. https:// doi. org/ 10. 1007/ 978-3- 319- 47319- 27. 1955–8. ISSN: 1558–4615. https:// doi. org/ 10. 1109/ EMBC. 2015. 73187 67. 67. Leng S, Tan RS, Chai KTC, Wang C, Ghista D, Zhong L. The electronic 85. Stewart M, Verkerk GA, Stafford KJ, Schaefer AL, Webster JR. Noninvasive stethoscope. Biomed Eng Online. 2015;14(1):66. https:// doi. org/ 10. assessment of autonomic activity for evaluation of pain in calves, using 1186/ s12938- 015- 0056-y.4. surgical castration as a model. J Dairy Sci. 2010;93(8):3602–9. https:// 68. Goovaerts HG, van Geijn HP, Rompelman O. An inductive sensor for doi. org/ 10. 3168/ jds. 2010- 3114. recording of fetal movements and sounds. In: 1991 Annu Int Conf IEEE 86. Stewart M, Shepherd HM, Webster JR, Waas JR, McLeay LM, Schutz KE. Eng Med Biol Soc; 1991. p. 1622–3. https:// doi. org/ 10. 1109/ IEMBS. 1991. Eec ff t of previous handling experiences on responses of dairy calves to 684669. routine husbandry procedures. Animal. 2013;7(5):828–33. https:// doi. 69. Martinek R, Nedoma J, Fajkus M, Kahankova R, Konecny J, Janku P, et al. org/ 10. 1017/ S1751 73111 20022 5X. A phonocardiographic-based fiber-optic sensor and adaptive filtering 87. Hagen K, Langbein J, Schmied C, Lexer D, Waiblinger S. Heart rate vari- system for noninvasive continuous fetal heart rate monitoring. Sensors. ability in dairy cows—influences of breed and milking system. Physiol 2017;17(4):890. https:// doi. org/ 10. 3390/ s1704 0890. Behav. 2005;85(2):195–204. https:// doi. org/ 10. 1016/j. physb eh. 2005. 03. 70. Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable 019. photoplethysmography sensors and their potential future applica- 88. Zucca E, Ferrucci F, Di Fabio V, Croci C, Ferro E. The use of electrocar- tions in health care. Int j biosens. 2018;4(4):195–202. https:// doi. org/ 10. diographic recording with Holter monitoring during treadmill exercise 15406/ ijbsbe. 2018. 04. 00125. to evaluate cardiac arrhythmias in racehorses. Vet Res Commun. 71. Kamal AAR, Harness JB, Irving G, Mearns AJ. Skin photoplethysmogra- 2003;27(Suppl 1):811–4. https:// doi. org/ 10. 1023/B: VERC. 00000 14278. phy—a review. Comput Methods Programs Biomed. 1989;28(4):257–69. 44422. 9d. https:// doi. org/ 10. 1016/ 0169- 2607(89) 90159-4. 89. Langbein J, Nürnberg G, Manteuffel G. Visual discrimination learning in 72. Giovangrandi L, Inan OT, Wiard RM, Etemadi M, Kovacs GTA. Ballisto- dwarf goats and associated changes in heart rate and heart rate vari- cardiography—A method worth revisiting. In: 2011 Annu Int Conf IEEE ability. Physiol Behav. 2004;82(4):601–9. https:// doi. org/ 10. 1016/j. physb Eng Med Biol Soc; 2011. p. 4279–82. ISSN: 1558–4615. https:// doi. org/ eh. 2004. 05. 007. 10. 1109/ IEMBS. 2011. 60910 62. 90. Von Lewinski M, Biau S, Erber R, Ille N, Aurich J, Faure JM, et al. Cortisol 73. Wang F, Tanaka M, Chonan S. Development of a PVDF piezopolymer release, heart rate and heart rate variability in the horse and its rider: sensor for unconstrained in-sleep cardiorespiratory monitoring. J Intell different responses to training and performance. Vet J. 2013;197(2):229– Mater Syst Struct. 2003;14(3):185–90. https:// doi. org/ 10. 1177/ 10453 32. https:// doi. org/ 10. 1016/j. tvjl. 2012. 12. 025. 89X03 01400 3006. 91. Schmied C, Boivin X, Waiblinger S. Stroking different body regions 74. Niizeki K, Nishidate I, Uchida K, Kuwahara M. Unconstrained cardiorespi- of dairy cows: effects on avoidance and approach behavior toward ratory and body movement monitoring system for home care. Med Biol humans. J Dairy Sci. 2008;91(2):596–605. https:// doi. org/ 10. 3168/ jds. Eng Comput. 2005;43(6):716–24. https:// doi. org/ 10. 1007/ BF024 30948.2007- 0360. 75. Koivistoinen T, Junnila S, Varri A, Koobi T. A new method for measuring 92. Sudhakara Reddy B, Sivajothi S. Analysis of base apex lead electrocar- the ballistocardiogram using EMFi sensors in a normal chair. In: 2004 diograms of adult buffaloes. J Dairy Vet Anim. 2015. https:// doi. org/ 10. Annu Int Conf IEEE Eng Med Biol Soc. vol. 1; 2004. p. 2026–9. https:// doi. 15406/ jdvar. 2015. 02. 00058. org/ 10. 1109/ IEMBS. 2004. 14035 96. 93. Mohr E, Langbein J, Nurnberg G. Heart rate variability: a noninva- 76. Kärki S, Lekkala J. A new method to measure heart rate with EMFi and sive approach to measure stress in calves and cows. Physiol Behav. PVDF materials. J Med Eng Technol. 2009;33(7):551–8. https:// doi. org/ 2002;75(1):251–9. https:// doi. org/ 10. 1016/ S0031- 9384(01) 00651-5. 10. 1080/ 03091 90090 30674 24. K ahankova et al. Animal Biotelemetry (2022) 10:15 Page 21 of 21 94. Verheyen T, Decloedt A, De Clercq D, Deprez P, Sys S, van Loon G. 114. Tan Yf, Du L. Study on wavelet transform in the processing for ECG Electrocardiography in horses, part 1: how to make a good recording. signals. In: 2009 WRI World Congress on Software Engineering. vol. 4; Vlaams Diergeneeskd Tijdschr. 2010;79(5):331–6. 2009. p. 515–8. https:// doi. org/ 10. 1109/ WCSE. 2009. 89. 95. Janzekovic M, Vindis P, Stajnko D, Brus M. Polar sport tester for cattle 115. Martin Bland J, Altman D. Statistical methods for assessing agree- heart rate measurements. IntechOpen. 2010. https:// doi. org/ 10. 5772/ ment between two methods of clinical measurement. Lancet. 10356. 1986;327(8476):307–10. https:// doi. org/ 10. 1016/ S0140- 6736(86) 96. Tajik J, Samimi A, Shojaeepour S, Jarakani S. Analysis of base-apex 90837-8. lead electrocardiogram in clinically healthy Kermani sheep. Istanbul 116. Jackson P, Cockcroft P. Clinical examination of farm animals. John Wiley Universitesi Veteriner Fakultesi Dergisi. 2015;42(1):74–9. https:// doi. org/ & Sons; 2008. ISBN 0-632-05706-8. 10. 16988/ iuvfd. 2016. 77664. 117. Marjan J, Prišenk J, Mursec B, Vindis P, Stajnko D, Cus F. The art equip- 97. Costa CFd, Samesima N, Pastore CA. Cardiac mean electrical axis in thor- ment for measuring the horse’s heart rate. J Achiev Mater Manuf. oughbreds—standardization by the Dubois lead positioning system. 2010;41:180–6. PLoS ONE. 2017;12(1):e0169619. https:// doi. org/ 10. 1371/ journ al. pone. 118. Martinek R, Brablik J, Kolarik J, Ladrova M, Nedoma J, Kahankova R, 01696 19. et al. A Comparison between novel FPGA-based pad monitoring 98. Forin-Wiart MA, Enstipp MR, Maho LEY, Handrich Y. Why implantation system using ballistocardiography and the conventional systems for of bio-loggers may improve our understanding of how animals cope synchronization and gating of CMRI at 3 Tesla: a pilot study. IEEE Access. within their natural environment. Integr Zool. 2019;14(1):48–64. https:// 2020;8:4149–70. https:// doi. org/ 10. 1109/ ACCESS. 2019. 29632 21. doi. org/ 10. 1111/ 1749- 4877. 12364. 119. Mugdha AC, Rawnaque FS, Ahmed MU. A study of recursive least 99. Bjarnason Gunnarsson A, Arnason T, Oddgeirsson M, Sigmarsson squares (RLS) adaptive filter algorithm in noise removal from ECG sig- AB, Gunnarsson A. Validation of ECG-derived heart rate recordings nals. In: 2015 Int. Conf. Inform. Electron. Vis. (ICIEV ); 2015. p. 1–6. https:// in Atlantic cod (Gadus morhua L.) with an implantable data logging doi. org/ 10. 1109/ ICIEV. 2015. 73339 98 system. Anim Biotelemetry. 2019;7(1):13. https:// doi. org/ 10. 1186/ 120. Sharma B, Suji RJ. ECG denoising using Weiner filter and adaptive least s40317- 019- 0176-4. mean square algorithm. In: 2016 IEEE IEEE Int. Conf. Recent Trends 100. Svendsen E, Fore M, Okland F, Grans A, Hedger RD, Alfredsen JA, et al. Electron. Inf. Commun. Technol. RTEICT 2016-Proc. p. 53–7. https:// doi. Heart rate and swimming activity as stress indicators for Atlantic org/ 10. 1109/ RTEICT. 2016. 78077 81. salmon (Salmo salar). Aquaculture. 2021;531: 735804. https:// doi. org/ 10. 121. Kahankova R, Martinek R, Jaros R, Behbehani K, Matonia A, Jezewski M, 1016/j. aquac ulture. 2020. 735804. et al. A review of signal processing techniques for non-invasive fetal 101. McGaw IJ, Steell SC, Leeuwen TEV, Eliason EJ, Cooke SJ. Application of electrocardiography. IEEE Rev Biomed Eng. 2020;13:51–73. https:// doi. miniature heart rate data loggers for use in large free-moving decapod org/ 10. 1109/ RBME. 2019. 29380 61. crustaceans: method development and validation. Physiol Biochem 122. Barnova K, Martinek R, Jaros R, Kahankova R. Hybrid methods based on Zool. 2018;91(1):731–9. https:// doi. org/ 10. 1086/ 695839. empirical mode decomposition for non-Invasive fetal heart rate moni- 102. Palacios C, Plaza J, Abecia JA. A high cattle-grazing density alters circa- toring. IEEE Access. 2020;8:51200–18. https:// doi. org/ 10. 1109/ ACCESS. dian rhythmicity of temperature, heart rate, and activity as measured by 2020. 29802 54. implantable bio-loggers. Front Physiol. 2021. https:// doi. org/ 10. 3389/ 123. Martinek R, Kahankova R, Jaros R, Barnova K, Matonia A, Jezewski M, fphys. 2021. 707222. et al. Non-invasive fetal electrocardiogram extraction based on novel 103. Fuchs B, Sørheim KM, Chincarini M, Brunberg E, Stubsjøen SM, Bratberg- hybrid method for intrapartum ST segment analysis. IEEE Access. sengen K, et al. Heart rate sensor validation and seasonal and diurnal 2021;9:28608–31. https:// doi. org/ 10. 1109/ ACCESS. 2021. 30587 33. variation of body temperature and heart rate in domestic sheep. Vet 124. Graupe D, Veselinovic D. Blind adaptive filtering of speech from noise of Anim Sci. 2019;8: 100075. https:// doi. org/ 10. 1016/j. vas. 2019. 100075. unknown spectrum using a virtual feedback configuration. IEEE Trans 104. Selesnick IW, Burrus CS. Generalized digital Butterworth filter design. Speech Audio Process. 2000;8(2):146–58. https:// doi. org/ 10. 1109/ 89. IIEEE Trans Image Process. 1998;46(6):1688–94. https:// doi. org/ 10. 1109/ 824699. 78. 678493. 125. Yen KC, Zhao Y. Adaptive co-channel speech separation and recogni- 105. Guo-ping H, Ling-juan M, Hui D. A new method for effcient design of tion. IEEE Trans Speech Audio Process. 1999;7(2):138–51. https:// doi. Butterworth filter based on symbolic calculus. In: 2010 International org/ 10. 1109/ 89. 748119. Conference on Computer Application and System Modeling (ICCASM 126. Widrow B, Glover JR, McCool JM, Kaunitz J, Williams CS, Hearn RH, 2010). vol. 8; 2010. p. V8–111V8114. ISSN: 2161–9077. https:// doi. org/ 10. et al. Adaptive noise cancelling: principles and applications. Proc IEEE. 1109/ ICCASM. 2010. 56191 30. 1975;63(12):1692–716. https:// doi. org/ 10. 1109/ PROC. 1975. 10036. 106. Zhongshen L. Design and analysis of improved Butterworth low pass filter. In: 2007 8th Int Conf Electr Meas Inst. 2007. p. 1-7291732. https:// Publisher’s Note doi. org/ 10. 1109/ ICEMI. 2007. 43505 54 Springer Nature remains neutral with regard to jurisdictional claims in pub- 107. Jagtap SK, Uplane MD. The impact of digital filtering to ECG analysis: lished maps and institutional affiliations. Butterworth filter application. In: 2012 Int Conf Commun Inf Comput Technol. 2012. p. 1–6. https:// doi. org/ 10. 1109/ ICCICT. 2012. 63981 45. 108. Gaikwad KM, Chavan MS. Removal of high frequency noise from ECG signal using digital IIR Butterworth filter. In: 2014 IEEE Global Confer - ence on Wireless Computing Networking (GCWCN); 2014. p. 121–4. https:// doi. org/ 10. 1109/ GCWCN. 2014. 70308 61. Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : 109. Martinek R, Kahankova R, Martin B, Nedoma J, Fajkus M. A novel modu- lar fetal ECG STAN and HRV analysis: towards robust hypoxia detection. fast, convenient online submission Technol Health Care. 2019;27(3):257–87. https:// doi. org/ 10. 3233/ thorough peer review by experienced researchers in your ﬁeld THC- 181375. 110. McDonnell JTE, Bentley PM. Wavelet transforms: an introduction. Elec- rapid publication on acceptance tron Commun Eng J. 1994;6(4):175–86. https:// doi. org/ 10. 1049/ ecej: support for research data, including large and complex data types 19940 401. • gold Open Access which fosters wider collaboration and increased citations 111. Rioul O, Vetterli M. Wavelets and signal processing. IEEE Signal Process Mag. 1991;8(4):14–38. https:// doi. org/ 10. 1109/ 79. 91217. maximum visibility for your research: over 100M website views per year 112. Addison PS. Wavelet transforms and the ECG: a review. Physiol Meas. 2005;26(5):R155–99. https:// doi. org/ 10. 1088/ 0967- 3334/ 26/5/ R01. At BMC, research is always in progress. 113. Mukherjee A, Ghosh KK. An efficient wavelet analysis for ECG signal pro - Learn more biomedcentral.com/submissions cessing. In: 2012 Int. Conf. Inform. Electron. Vis. (ICIEV ); 2012. p. 411–5. https:// doi. org/ 10. 1109/ ICIEV. 2012. 63174 19.

Journal

Animal Biotelemetry – Springer Journals

Published: Apr 27, 2022

Keywords: Animal electrocardiography (ECG); Heart rate variability (HRV); Heart rate (HR); Animal welfare; Stress; Veterinary monitoring; Ballistocardiography (BCG); Farm animals

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Alternative measurement systems for recording cardiac activity in animals: a pilot study

Alternative measurement systems for recording cardiac activity in animals: a pilot study

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Alternative measurement systems for recording cardiac activity in animals: a pilot study

Alternative measurement systems for recording cardiac activity in animals: a pilot study

References (105)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies