Access the full text.
Sign up today, get DeepDyve free for 14 days.
A. Reisner, G. Clifford, R. Mark (2006)
The Physiological Basis of the Electrocardiogram
S. Severi, Matteo Fantini, L. Charawi, D. DiFrancesco (2012)
An updated computational model of rabbit sinoatrial action potential to investigate the mechanisms of heart rate modulationThe Journal of Physiology, 590
M. Teich, S. Lowen, B. Jost, K. Vibe-Rheymer, C. Heneghan (2000)
Heart Rate Variability: Measures and ModelsarXiv: Biological Physics
J. Kantelhardt, S. Havlin, P. Ivanov (2003)
Modeling transient correlations in heartbeat dynamics during sleepEPL, 62
James Heathers (2014)
Everything Hertz: methodological issues in short-term frequency-domain HRVFrontiers in Physiology, 5
P. Ivanov, L. Amaral, A. Goldberger, H. Stanley (1997)
Stochastic feedback and the regulation of biological rhythms.Europhysics letters, 43 4
R. Boer, J. Karemaker, J. Strackee (1985)
Spectrum of a series of point events, generated by the integral pulse frequency modulation modelMedical and Biological Engineering and Computing, 23
PW Macfarlane, A Oosterom, O Pahlm, P Kligfield, M Janse, J Camm (2010)
Comprehensive electrocardiology
Serge Winitzki (2003)
Computational Science and Its Applications — ICCSA 2003
S. Winitzki (2003)
Uniform Approximations for Transcendental Functions
Wilhelm Rosenberg, Theerasak Chanwimalueang, Tricia Adjei, U. Jaffer, Valentin Goverdovsky, D. Mandic (2017)
Resolving Ambiguities in the LF/HF Ratio: LF-HF Scatter Plots for the Categorization of Mental and Physical Stress from HRVFrontiers in Physiology, 8
G. Clifford, F. Azuaje, P. McSharry (2006)
Advanced Methods And Tools for ECG Data Analysis
F. Pin, V. Minero, F. Penna, M. Muscaritoli, R. Tullio, F. Baccino, P. Costelli (2017)
Interference with Ca2+-Dependent Proteolysis Does Not Alter the Course of Muscle Wasting in Experimental Cancer CachexiaFrontiers in Physiology, 8
AT Reisner, GD Clifford, RG Mark (2006)
Advanced methods and tools for ECG data analysis, Artech House engineering in medicine and biology series
(2012)
Principles of pharmacology: the pathophysiologic basis of drug therapy
V. Rosenberg, Wilhelm Christopher (2017)
Heads and hearts : establishing the principles behind health monitoring from the ear canal
P. McSharry, Gari Clifford, Lionel Tarassenko, L. Smith (2002)
Method for generating an artificial RR tachogram of a typical healthy human over 24-hoursComputers in Cardiology
V. Maltsev, Y. Yaniv, A. Maltsev, M. Stern, E. Lakatta (2014)
Modern perspectives on numerical modeling of cardiac pacemaker cell.Journal of pharmacological sciences, 125 1
P. Laguna, L. Sörnmo (2004)
Modelling Heart Rate Variability
(1913)
Die Kinetik der Invertinwirkung
(2012)
Principles of pharmacology: the pathophysiologic basis of drug therapy. Monographs in population biology
Y. Kurata, I. Hisatome, S. Imanishi, T. Shibamoto (2002)
Dynamical description of sinoatrial node pacemaking: improved mathematical model for primary pacemaker cell.American journal of physiology. Heart and circulatory physiology, 283 5
Y. Arai, J. Saul, P. Albrecht, L. Hartley, L. Lilly, R. Cohen, W. Colucci (1989)
Modulation of cardiac autonomic activity during and immediately after exercise.The American journal of physiology, 256 1 Pt 2
M. Soliński, J. Gieraltowski, J. Zebrowski (2016)
Modeling heart rate variability including the effect of sleep stages.Chaos, 26 2
(2019)
Proceedings of the sixteenth international symposium on mathematical theory of networks and systems
M. Malik, J. Bigger, A. Camm, R. Kleiger, A. Malliani, A. Moss, P. Schwartz (1996)
Heart rate variability. Standards of measurement, physiological interpretation, and clinical useEuropean Heart Journal, 17
T. Opthof, Ruben Coronel (2000)
The normal range and determinants of the intrinsic heart rate in man.Cardiovascular research, 45 1
P. Bressloff (2014)
Stochastic Processes in Cell BiologyInterdisciplinary Applied Mathematics
M. Dura, Johannes Schröder-Schetelig, S. Luther, S. Lehnart (2014)
Toward panoramic in situ mapping of action potential propagation in transgenic hearts to investigate initiation and therapeutic control of arrhythmiasFrontiers in Physiology, 5
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Heart rate variability (HRV) is governed by the autonomic nervous system (ANS) and is routinely used to estimate the state of body and mind. At the same time, recorded HRV features can vary substantially between people. A model for HRV that (1) correctly simulates observed HRV, (2) reliably functions for multiple scenarios, and (3) can be personalised using a manageable set of parameters, would be a significant step forward toward understanding individual responses to external influences, such as physical and physiological stress. Current HRV models attempt to reproduce HRV characteristics by mimicking the statistical properties of measured HRV signals. The model presented here for the simulation of HRV follows a radically different approach, as it is based on an approximation of the physiology behind the triggering of a heart beat and the biophysics mechanisms of how the triggering process—and thereby the HRV—is governed by the ANS. The model takes into account the metabolisation rates of neurotransmitters and the change in membrane potential depending on transmitter and ion concentrations. It produces an HRV time series that not only exhibits the features observed in real data, but also explains a reduction of low frequency band-power for physically or psychologically high intensity scenarios. Furthermore, the proposed model enables the personalisation of input parameters to the physiology of different people, a unique feature not present in existing methods. All these aspects are crucial for the understanding and application of future wearable health.
Biomedical Engineering Letters – Springer Journals
Published: Aug 19, 2019
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.