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Hindawi Journal of Healthcare Engineering Volume 2019, Article ID 1614963, 7 pages https://doi.org/10.1155/2019/1614963 Research Article Classification and Assessment of the Patelar Reflex Response through Biomechanical Measures 1 1,2 1 Yolocuauhtli Salazar-Muñoz , G. Angelina Lo´pez-Pe´rez, Blanca E. Garcı´a-Caballero, 1 3 4 Refugio Muñoz-Rios, Luis A. Ruano-Caldero´n, and Leonardo Trujillo Tecnolo´gico Nacional de M´exico/Instituto Tecnolo´gico de Durango, C.P. 34080, Durango, DGO, Mexico Universidad Polit´ecnica de Durango, C.P. 34300, Durango, DGO, Mexico Servicios de Salud del Estado de Durango, Hospital General 450, C.P. 34206, Durango, DGO, Mexico Tecnolo´gico Nacional de M´exico/Instituto Tecnolo´gico de Tijuana, C.P. 22430, Tijuana, B.C., Mexico Correspondence should be addressed to Yolocuauhtli Salazar-Muñoz; ysalazar@itdurango.edu.mx Received 5 February 2019; Revised 15 May 2019; Accepted 19 June 2019; Published 9 July 2019 Guest Editor: Alessandro Mengarelli Copyright © 2019 Yolocuauhtli Salazar-Muñoz et al. ,is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Clinical evaluation of the patellar reflex is one of the most frequent diagnostic methods used by physicians and medical specialists. However, this test is usually elicited and diagnosed manually. In this work, we develop a device specifically designed to induce the patellar reflex and measure the angle and angular velocity of the leg during the course of the reflex test. We have recorded the response of 106 volunteers with the aim of finding a recognizable pattern in the responses that can allow us to classify each reflex according to the scale of the National Institute of Neurological Disorders and Stroke (NINDS). In order to elicit the patellar reflex, a hammer is attached to a specially designed pendulum, with a controlled impact force. All volunteer test subjects sit at a specific height, performing the Jendrassik maneuver during the test, and the medical staff evaluates the response in accordance with the NINDS scale. ,e data acquisition system is integrated by using a tapping sensor, an inertial measurement unit, a control unit, and a graphical user interface (GUI). ,e GUI displays the sensor behavior in real time. ,e sample rate is 5 kHz, and the control unit is configured for a continuous sample mode. ,e measured signals are processed and filtered to reduce high-frequency noise and digitally stored. After analyzing the signals, several domain-specific features are proposed to allow us to differentiate between various NINDS groups using machine learning classifiers. ,e results show that it is possible to automatically classify the patellar reflex into a NINDS scale using the proposed biomechanical measurements and features. stretching, which is caused when the hammer stroke is 1. Introduction applied [3, 4]. ,e normal response must be a sudden leg ,e observation of the patellar reflex is one of the clinical extension. A reduction or exaggeration of the response is trials performed most frequently for neurological tests, indicators of damage or interruption in the innervation of making it an essential tool for the diagnosis of many neu- the quadriceps muscle [5]. romuscular diseases [1]. ,e result of the test is commonly rated using the scales ,e patellar reflex is a deep tendon reflex, mediated by of the National Institute of Neurological Disorders and the spinal nerves from the levels L2, L3, and L4 in the spinal Stroke (NINDS) and the Mayo Clinic [6]; in this work, we cord, predominantly in the root L4. ,e patellar reflex test is use the former one. ,is scale measures the response performed to determine the integrity of the neurological magnitude assigning a different number of “crosses” (+), function, which is accomplished by hitting the patellar whereby zero crosses (0+) indicate an exam with no visible tendon below the knee cap with a test hammer [2]. answer; one cross (1+) corresponds to a slight reflex; two ,e patellar reflex occurs when an abrupt change arises crosses (2+) indicate a reflex in the lower half of the normal in muscle length; in this case, it is produced by the tendon range; three crosses (3+) are a reflex in the upper half of the 2 Journal of Healthcare Engineering normal range; and four crosses (4+) mean the reflex is 2.1.2. Data Acquisition System (DAS). ,e DAS is composed significantly enhanced [6]. of the following elements: An alteration of the patellar reflex response may be (i) Tapping Sensor. ,e LDT0-028K piezoelectric sen- caused by several different factors, which can range from sor manufactured by Measurement Specialities was tumors in the spinal cord [7] to diseases, such as the used, connected to a charge amplifier circuit and an Guillain–Barre syndrome [8] that affects the peripheral instrumentation amplifier to obtain a 5 V pulse, thus nervous system [9]. Likewise, there are other factors that can detecting the instant of impact on the tendon to disturb the test result, such as the intensity of the stroke [10], synchronize the other measured variables. the nervousness that the patient may experiment during the (ii) Angular Displacement and Rate Sensor. ,e test, and the age of the patient [11]. Sparkfun IMU number SEN-11072 was used, which ,e development of an objective quantification for the has 5 degrees of freedom. It contains IDG500 2-axis test is a goal that has arisen in recent years [10, 12–14]. Some gyroscope with the sensitivity set to 2 mV/ /s and work has attempted to quantify the test by performing ADXL335 3-axis accelerometer. motion analysis [15] in cerebral palsy children [16] and also proposed a new iPhone application to measure the reflex (iii) Control Unit. ,e signals from the sensors are response [17]. Other studies have attempted to model the captured by the NI USB6009 acquisition board, patellar reflex as a response from a theoretical second-order using two analogue channels and a power source of system [18]. 5 V for the electronic system. In a previous work, this research team designed a (iv) Graphical User Interface (GUI). ,e GUI was device to measure, digitally store, and display the patellar designed in LabView to display the sensor readings reflex response [19], capturing the relation between ve- in real time and save the captured signals of each test locity and the magnitude of the response [20]. ,e aim of in an lvm file. Each new test generates a new file that this study is to analyze the captured biomechanical var- is then imported into Matlab for later analysis. ,e iables, including the angle of the knee, the velocity of the selected sample rate is 5 kHz, and the board is knee movement, the applied force, and the magnitude of configured for a continuous sample mode. ,e GUI the reflex response, in order to develop an automatic shows the following indicators in real time: the classification algorithm using digital signal processing and angular displacement, the angular velocity, and the machine learning algorithms. moment of impact on the tendon. 2.2. Volunteer Selection. In this work, we use a group of 106 2. Materials and Methods healthy volunteers to evaluate our proposed system. All of 2.1. Setup of the Measurement System. According to the them are students from the Faculty of Medicine at the previous works of Salazar-Muñoz et al. [19, 20] and Moreno- “Universidad Juarez ´ del Estado de Durango,” and they in- Estrada et al. [21], the designed device uses an impact sensor clude both men and women. ,e mean age, height, and body as the start time marker of the test and an inertial mea- mass for subjects were 21.5± 1.2 years, 1.73± 0.09 m, and surement unit (IMU) to measure both the angular velocity 72± 13 kg, respectively. A volunteer is considered to be and angular position of the leg after it receives the hammer healthy for this study if he is not suffering from any di- stroke on the tendon. ,e measurement system consists of agnosed neurological or neuromuscular disease when the the following two parts. test is realized [22]. ,e clinical trial was carried out under the direction of the Neurology Department of the “Hospital General 450” of Durango City, Mexico. ,e study was ap- proved by the Ethics and Research Committee from the 2.1.1. Mechanical Controlled Force System. ,e mechanical hospital. controlled force system consists of a hammer designed as a Charpy pendulum. ,e mechanical system consists of an aluminium pendulum rubber tip attached to a toothed gear 2.3. Measurement Procedure. Experimental tests were per- angle with an adjustable height for the hammer initial po- formed under the supervision of the physician. Two reflex sition, which allows you to select the impact force on the tests are applied to every volunteer to develop an automatic patellar tendon as a function of the elevation angle of the classification algorithm using digital signal processing and pendulum. ,e tip is the same as the clinical hammer used by machine learning algorithms. We compare the NINDS scale a physician. ,e physician shall place the arm in the desired with the biomechanical variables registered by the designed position and release it manually. ,e force applied will be the measurement system. ,e volunteer must be seated in a high same for all test subjects to generate their own flexion. ,e chair, this way his right foot never touches the floor. In order prototype was designed such that the elevation angle can to get a high relaxation of the quadriceps muscle, the vol- ° ° ° increase from 30 to 165 in steps of 15 . In these experiments, unteer is requested to perform the Jendrassik maneuver [23]. the hammer arm was elevated to 135 and the hammer mass All the tests were performed under the same conditions. was 195 gr, resulting in an impact force of 0.82 N, which was validated by the Charpy pendulum equation at the me- (i) Test A. A physician gives a sharp tap on the patellar chanical engineering laboratory [21]. tendon with a standard clinical hammer. ,e Journal of Healthcare Engineering 3 physician evaluates the reflex response using the 2.5. Classification. To achieve the classification of the re- NINDS scale. Dafkin et al. [10] established using alized patellar reflex tests based on the number of crossings stepwise multiple regression analysis that different in the NINDS scale, basic pattern recognition and machine groups of subjective raters all relied on the change learning methods are used [25, 26]. Specifically, the fol- of the knee angle to assess the reflex. ,erefore, lowing four classifiers are used: the trained physician was asked to focus on this (i) Naive Bayes feature to provide his rating for the analyzed (ii) Tree BAGGER patients. (iii) k-nearest neighbors (KNN) (ii) Test B. After Test A, the sensors are placed on the leg of the volunteer as shown in Figure 1, and the (iv) Support vector machine (SVM) procedure is as follows: (a) the taping sensor Classifiers are tested with different combinations of the (impact sensor) is adhered to the patellar tendon extracted features. Because the size of the dataset is relatively with tape, below the patella to avoid any undesired small, each classifier is tested using leave-one-out cross movements and (b) the IMU is placed on the ankle validation. Moreover, the data are preprocessed for feature using a belt. ,e distance between the knee centre of reduction using principal component analysis (PCA). rotation and location of the sensor in all subjects was maintained small following the reference [24]. ,e IMU must be positioned parallel to the leg and 3. Results and Discussion perpendicular to the floor. ,e controlled force According to the assessment given by the hospital staff at the system hits the patellar tendon. ,e data acquisition “Hospital General 450,” the collected samples are distributed system stores all sensor readings using the GUI that in the NINDS scale as follows: 8 samples belong to the 0+ was designed for this experiment. After this pro- level, 20 samples were from 1+ level, 48 samples from 2+ cedure, the measurement system is withdrawn from level, and 30 samples belong to 3+ level. ,e 4+ level is the leg. ,is test was performed under the physician omitted because none of the volunteers exhibited such a who verifies that the reflex response was equivalent response. to Test A. No test was rejected because it ranked First, we analyze the recorded signals from each response differently from the Test A. level, to determine if there are any general similarities be- tween them. In Figures 4 and 5, we can see the average 2.4. Data Treatment and Features. ,e data stored by the angular position and angular velocity, grouped based on the system contain three time series. ,e first one is the impact corresponding NINDS levels. signal, which marks the exact moment when the pendulum hits Figure 4 shows that the movement of the leg after the the tendon, denoted by t . ,e second time series is the angular impact has a wavelike behavior, which decreases with time position signal, which measures the angle of the leg during the until it stabilizes to the rest position. ,e maximum am- reflex response. ,e third time series is the angular velocity of plitude reached by the corresponding average signal of the the leg movement during the test. All the signals are trimmed to 3+ group is 47 degrees. ,is peak corresponds to the only extract the 4 seconds following the hammer impact, after maximum elevation of the leg. ,e minimum average value of the same group is −37.85 degrees, corresponding to the t , because the signal power has decreased by 97% and all the rd vector lengths were equal. A low pass 3 degree Chebyshev retraction of the leg after the lift. ,is value, which is the Δa feature, is decreased by 36% in the corresponding average filter with a cutoff frequency of 100 Hz was used to eliminate high-frequency noise. signal of the 2+ group, by 74% in the corresponding mean Afterward, the signals of the angular position and an- signal of the 1+ group, and by 97% in the corresponding gular velocity are characterized by extracting the following mean signal of 0+ group, with respect to the mean signal of set of descriptive features. ,e extracted features are sum- the 3+ group. marized in Figure 2 for the angular position and in Figure 3 In Figure 5, the maximum value reached by the average for the angular velocity, each case showing a typical signal of the velocity signals of 3+ is 38 degrees per second. ,is captured by the system for each measurement. value is the V feature and is attenuated by 31% in the max From the angular position signal, the extracted features mean signal of the 2+ group, by 76% for the 1+ group, and by are as follows. First,Δa represents the difference between the 95% for the 0+ group [20]. maximum and minimum peaks of the signal. Second,Δ1/3 is In Table 1, we can observe the mean and standard the ratio between the first (P1) and third peak (P3) of the deviation of the grouped features according to the NINDS signal. ,ird, Δt is the time interval between the maximum scale. and the minimum peaks. Fourth, Δt is the time interval To make sure the separation between groups is sig- between the first peak and the third peak of the signal. nificant, the Kruskal–Wallis statistical test is applied to Finally, T is the settling time, which is the moment when the every feature. ,e test is chosen because the data distri- signal power has decreased by 97%. bution is not Gaussian. ,e test gives a p value <<0.05 in In the case of the angular velocity, a single feature is every test, allowing us to reject the null hypothesis that all extracted called V , which is the maximum value of the samples share the same median. Figure 6 shows the box- max signal, shown in Figure 3 as the highest peak. plots for each NINDS level for theΔa feature, and Figure 7 4 Journal of Healthcare Engineering Graphical user interface Data acquisition system Mechanical controlled force system Tapping sensor Inertial measurement unit Figure 1: Schematic representation of the experimental system to obtain the patellar reŒex response, showing the physical setup and sensor locations. 30 60 P1 ∆1/3 = P1/P3 P3 20 ∆a T –10 ∆t −20 –20 ∆t −40 –30 0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.5 1 1.5 2 2.5 3 3.5 4 Time (s) Time (s) 0+ 2+ Figure 2: Features extracted from the angular position signal. 1+ 3+ Figure 4: Mean signals of each NINDS group for angular position readings. max Dierent combinations of features are selected based on the statistical results and used as the input data for the machine learning classiers. e tests are carried out using leave-one-out cross validation (LOO CV), given the rela- tively low number of samples in the database. Table 2 shows –5 all of the tested combinations and the classication accuracy –10 of each classier. In each case, principal component analysis 0 0.5 1 1.5 2 2.5 3 3.5 4 (PCA) is applied to the input features to perform feature Time (s) transformation (but results are only shown for the case in Figure 3: Feature extracted from the angular velocity signal. which PCA improved the performance of at least one classier). Best performance is achieved when using the Δa and V features with the naive Bayes classier without shows the same box plot for V feature. ese features are max max PCA, with only 11 of 106 misclassications, representing a the ones that show the most separation between all the classication accuracy of 89.62%. NINDS groups. Angular velocity (°/s) Angular position (°) Angle (°) Journal of Healthcare Engineering 5 –10 –20 0 0.5 1 1.5 2 2.5 3 3.5 4 Time (s) 0+ 2+ 1+ 3+ Figure 5: Mean signals of each NINDS group for angular velocity readings. Table 1: Mean and standard deviation (mean ± std) of the features for each NINDS group. NINDS scale Δa Δ1/3 Δt (ms) Δt (ms) T (sec) V 1 2 s max 0+ 3.45 ± 1.93 0.82 ± 0.3 108 ± 71 1.78 ± 0.244 0.89 ± 0.318 2.73 ± 1.96 1+ 24.52 ± 8.4 0.144 ± 0.12 354 ± 68 1.57 ± 0.164 1.97 ± 0.766 10.34 ± 5.06 2+ 59.57 ± 12.41 0.156 ± 0.16 414 ± 64 1.73 ± 0.173 2.41 ± 0.785 26.97 ± 9.66 3+ 93.83 ± 18.39 0.135 ± 0.16 440 ± 52 1.79 ± 0.222 2.53 ± 0.773 38.71 ± 9.53 140 Table 2: Classication accuracy for dierent feature combinations, showing the LOO CV testing performance. Naive Tree KNN SVM Bayes BAGGER (%) (%) (%) (%) Δa, V 89.62 82.07 86.79 67.92 40 max Δa, V (with PCA) 88.64 83.96 86.79 66.98 max Δa, V , Δ1/3 84.9 86.79 83.96 69.81 max Δa, T 86.79 84.9 35.84 71.69 0+ 1+ 2+ 3+ Δ1/3, Δt , Δt 40.56 53.77 53.77 34.9 1 2 NINDS groups Δ1/3, Δt , Δt 1 2 57.54 55.66 52.86 40.56 Figure 6: Boxplots of the Δa feature for each NINDS group. (with PCA) 0+ 1+ 2+ 3+ –10 NINDS groups 0 20 40 60 80 100 120 140 Figure 7: Boxplots of the V feature for each NINDS group. Δa (°) max 0+ 3+ Figure 8 shows all of the data samples plotted in the Δa 1+ Misclassified 2+ and V feature space. e points are labeled to show the max correctly classied sample from each group, using a dierent Figure 8: Δa and V feature space, showing all the samples max mark for each NINDS level and the misclassied samples as collected in the dataset. e dark round markers shows mis- well. Notice that most of the classication errors can be classied tests by naive Bayes classier, and all other points were found on the boundary between the 2+ and 3+ groups. correctly classied into their respective groups. Δa (°) V (°/s) max Angular velocity (°/s) V (°/s) max 6 Journal of Healthcare Engineering [6] S. Manschot, L. van Passel, E. Buskens, A. Algra, and 4. Conclusion J. van Gijn, “Mayo and NINDS scales for assessment of tendon reflexes: between observer agreement and implications for ,e dynamic behavior of the leg during the patellar reflex communication,” Journal of Neurology, Neurosurgery & creates movement patterns that can be automatically classified Psychiatry, vol. 64, no. 2, pp. 253–255, 1998. in the NINDS scale with a useful degree of accuracy. ,is is [7] M. Mendioroz Iriarte and J. J. Poza Aldea, “Mielopat´ıa y shown to be possible using a straightforward feature ex- ´ ´ radiculopatıa por cervicoartrosis: tumores de la medula traction procedure and pattern recognition techniques. ,e espinal,” Medicine—Programa de Formacion ´ M´edica Con- classification methods used in this study achieved a LOO CV tinuada Acreditado, vol. 8, no. 99, pp. 5339–5344, 2003. test accuracy of 89.62% in the best case, using only two feature [8] F. Micheli, Tratado de Neurolog´ıa Clinica, Medica Pan- dimensions and the naive Bayes classifier. However, despite americana, Madrid, Spain, 2002. the good performance by the proposed system, discordance [9] E. B. Kelly, Encyclopedia of Human Genetics and Disease, between clinical measurements and the current measure- vol. 1, ABC-CLIO, Greenwood, Santa Barbara, CA, USA, ments might still be considered high in some scenarios. Moreover, the proposed approach should be verified using [10] C. Dafkin, A. Green, S. Kerr, D. Veliotes, and W. Mckinon, “,e accuracy of subjective clinical assessments of the patellar observations from different neurologists to determine how reflex,” Muscle & Nerve, vol. 47, no. 1, pp. 81–88, 2013. well this approach generalized across experts. Nonetheless, [11] A. Chandrasekhar, A. O. Noor Azuan, L. K. ,am, K. S. Lim, the proposed system might lead to the full automatization of and W. A. B. Wan Abas, “Influence of age on patellar tendon this test by integrating these future improvements, along with reflex response,” PLoS One, vol. 8, no. 11, Article ID e80799, other promising technical enhancements, such as wireless sensors to increase a patient’s comfort or edge computing to [12] M. K. Lebiedowska, S. Sikdar, A. Eranki, and L. Garmirian, simplify the data processing and transmission process. “Knee joint angular velocities and accelerations during the patellar tendon jerk,” Journal of Neuroscience Methods, Data Availability vol. 198, no. 2, pp. 255–259, 2011. [13] S. G. Chung, E. M. van Rey, Z. Bai, M. W. Rogers, E. J. Roth, ,e data used to support the findings of this study are and L.-Q. Zhang, “Aging-related neuromuscular changes available from the corresponding author upon request. characterized by tendon reflex system properties,” Archives of Physical Medicine and Rehabilitation, vol. 86, no. 2, pp. 318–327, 2005. Disclosure [14] V. A. 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Journal of Healthcare Engineering – Hindawi Publishing Corporation
Published: Jul 9, 2019
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