Mathematical model of the photoplethysmogram for testing methods of biological signals analysis
Izvestiya VUZ. Applied Nonlinear Dynamics, Tome 31 (2023) no. 5, pp. 586-596.

Voir la notice de l'article provenant de la source Math-Net.Ru

The purpose of this study was to develop a mathematical model of the photoplethysmogram, which can be used to test methods that introduce the instantaneous phases of the modulating signals. The model must reproduce statistical and spectral characteristics of the real photoplethysmogram, and explicitly incorporate the instantaneous phases of the modulating signals, so they can be used as a reference during testing. Methods. Anacrotic and catacrotic phases of the photoplethysmogram pulse wave were modeled as a sum of two density distributions for the skew normal distribution. The modulating signals were introduced as harmonic functions taken from the experimental instantaneous phases of the VLF (0.015...0.04 Hz), LF (0.04...0.15 Hz) and HF (0.15...0.4 Hz) oscillations in the real photoplethysmogram. The spectral power in the VLF, LF, and HF frequency ranges was calculated to compare the model and experimental data. Results. The model qualitatively reproduces the shape of the experimental photoplethysmogram pulse wave and shows less than 1% error when simulating the spectral properties of the signal. Conclusion. The proposed mathematical model can be used to test the methods for introduction of the instantaneous phases of the modulating signals in photoplethysmogram time-series.
Keywords: mathematical modeling, photoplethysmogram, phase analysis, spectral analysis, synchronization, directional coupling.
@article{IVP_2023_31_5_a4,
     author = {A. M. Vahlaeva and J. M. Ishbulatov and A. S. Karavaev and V. I. Ponomarenko and M. D. Prokhorov},
     title = {Mathematical model of the photoplethysmogram for testing methods of biological signals analysis},
     journal = {Izvestiya VUZ. Applied Nonlinear Dynamics},
     pages = {586--596},
     publisher = {mathdoc},
     volume = {31},
     number = {5},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a4/}
}
TY  - JOUR
AU  - A. M. Vahlaeva
AU  - J. M. Ishbulatov
AU  - A. S. Karavaev
AU  - V. I. Ponomarenko
AU  - M. D. Prokhorov
TI  - Mathematical model of the photoplethysmogram for testing methods of biological signals analysis
JO  - Izvestiya VUZ. Applied Nonlinear Dynamics
PY  - 2023
SP  - 586
EP  - 596
VL  - 31
IS  - 5
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a4/
LA  - ru
ID  - IVP_2023_31_5_a4
ER  - 
%0 Journal Article
%A A. M. Vahlaeva
%A J. M. Ishbulatov
%A A. S. Karavaev
%A V. I. Ponomarenko
%A M. D. Prokhorov
%T Mathematical model of the photoplethysmogram for testing methods of biological signals analysis
%J Izvestiya VUZ. Applied Nonlinear Dynamics
%D 2023
%P 586-596
%V 31
%N 5
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a4/
%G ru
%F IVP_2023_31_5_a4
A. M. Vahlaeva; J. M. Ishbulatov; A. S. Karavaev; V. I. Ponomarenko; M. D. Prokhorov. Mathematical model of the photoplethysmogram for testing methods of biological signals analysis. Izvestiya VUZ. Applied Nonlinear Dynamics, Tome 31 (2023) no. 5, pp. 586-596. http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a4/

[1] Gorshkov O., Ombao H., “Multi-chaotic analysis of inter-beat (R-R) intervals in cardiac signals for discrimination between normal and pathological classes”, Entropy (Basel), 23:1 (2021), 112 | DOI

[2] Fagard R. H., Stolarz K., Kuznetsova T., Seidlerova J., Tikhonoff V., Grodzicki T., Nikitin Y., Filipovsky J., Peleska J., Casiglia E., Thijs L., Staessen J. A., Kawecka-Jaszcz K., “Sympathetic activity, assessed by power spectral analysis of heart rate variability, in white-coat, masked and sustained hypertension versus true normotension”, J. Hypertens, 25:11 (2007), 2280–2285 | DOI

[3] Borovkova E. I., Prokhorov M. D., Kiselev A. R., Hramkov A. N., Mironov S. A., Agaltsov M. V., Ponomarenko V. I., Karavaev A. S., Drapkina O. M., Penzel T., “Directional couplings between the respiration and parasympathetic control of the heart rate during sleep and wakefulness in healthy subjects at different ages”, Front. Netw. Physiol., 2 (2022), 942700 | DOI

[4] Ponomarenko V. I., Prokhorov M. D., Karavaev A. S., Kiselev A. R., Gridnev V. I., Bezruchko B. P., “Synchronization of low-frequency oscillations in the cardiovascular system: Application to medical diagnostics and treatment”, The European Physical Journal Special Topics, 222:10 (2013), 2687–2696 | DOI

[5] Lefrandt J. D., Smit A. J., Zeebregts C. J., Gans R. O. B., Hoogenberg K. H., “Autonomic dysfunction in diabetes: a consequence of cardiovascular damage”, Current Diabetes Reviews, 6:6 (2010), 348–358 | DOI

[6] Dimitriev D. A., Saperova E. V., Dimitriev A. D., “State anxiety and nonlinear dynamics of heart rate variability in students”, PLoS ONE, 11:1 (2016), e0146131 | DOI

[7] Deka B., Deka D., “Nonlinear analysis of heart rate variability signals in meditative state: a review and perspective”, BioMedical Engineering OnLine, 22:1 (2023), 35 | DOI

[8] de Abreu R. M., Porta A., Rehder-Santos P., Cairo B., Sakaguchi C. A., da Silva C. D., Signini É. F., Milan-Mattos J. C., Catai A. M., “Cardiorespiratory coupling strength in athletes and non-athletes”, Respiratory Physiology Neurobiology, 305 (2022), 103943 | DOI

[9] Delliaux S., Ichinose M., Watanabe K., Fujii N., Nishiyasu T., “Muscle metaboreflex activation during hypercapnia modifies nonlinear heart rhythm dynamics, increasing the complexity of the sinus node autonomic regulation in humans”, Pflügers Archiv - European Journal of Physiology, 475:4 (2023), 527–539 | DOI

[10] Karavaev A. S., Skazkina V. V., Borovkova E. I., Prokhorov M. D., Hramkov A. N., Ponomarenko V. I., Runnova A. E., Gridnev V. I., Kiselev A. R., Kuznetsov N. V., Chechurin L. S., Penzel T., “Synchronization of the processes of autonomic control of blood circulation in humans is different in the awake state and in sleep stages”, Front. Neurosci., 15 (2022), 791510 | DOI

[11] Goldstein D. S., Bentho O., Park M.-Y., Sharabi Y., “Low-frequency power of heart rate variability is not a measure of cardiac sympathetic tone but may be a measure of modulation of cardiac autonomic outflows by baroreflexes”, Exp. Physiol., 96:12 (2011), 1255–1261 | DOI

[12] Natarajan A., Pantelopoulos A., Emir-Farinas H., Natarajan P., “Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study”, The Lancet Digital Health, 2:12 (2020), E650–E657 | DOI

[13] Ringwood J. V., Malpas S. C., “Slow oscillations in blood pressure via a nonlinear feedback model”, American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 280:4 (2001), R1105–R1115 | DOI

[14] Tang Q., Chen Z., Ward R., Elgendi M., “Synthetic photoplethysmogram generation using two Gaussian functions”, Sci. Rep., 10:1 (2020), 13883 | DOI

[15] McSharry P. E., Clifford G. D., Tarassenko L., Smith L. A., “A dynamical model for generating synthetic electrocardiogram signals”, IEEE Transactions on Biomedical Engineering, 50:3 (2003), 289–294 | DOI

[16] Cheng L., Khoo M. C. K., “Modeling the autonomic and metabolic effects of obstructive sleep apnea: a simulation study”, Front. Physiol., 2 (2012), 111 | DOI

[17] Kotani K., Struzik Z. R., Takamasu K., Stanley H. E., Yamamoto Y., “Model for complex heart rate dynamics in health and diseases”, Phys. Rev. E, 72:4 (2005), 041904 | DOI