ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Nikulina M. V., Antonets V. A. Experience in assessing heart rate variability by smoothed cardiointervalograms. Izvestiya VUZ. Applied Nonlinear Dynamics, 2022, vol. 30, iss. 2, pp. 176-188. DOI: 10.18500/0869-6632-2022-30-2-176-188

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
(downloads: 552)
Full text PDF(En):
(downloads: 396)
Article type: 
612.176:4, 004.932

Experience in assessing heart rate variability by smoothed cardiointervalograms

Nikulina Marina Valentinovna, Institute of Applied Physics of the Russian Academy of Sciences
Antonets Vladimir Aleksandrovich, Institute of Applied Physics of the Russian Academy of Sciences

The objective of this study is to show the possibility of using the smoothing cardiointervalograms (CIG) method which is solely time domain analysis of CIG to separate and display the influence of various mechanisms of human physiological regulation systems on his heart rate. Methods.This paper shows the possibility of using the method of smoothing the cardiointervalogram by means of a moving average for its subsequent decomposition into slow and fast components. Decomposition results are visualized by line graphs and pseudo-phase portraits. Visualization settings allow us to isolate unique transients and calculate its timing. The method is applied to data obtained under different subject functional states and differing in the level of adaptation risks, the presence or absence of stress. For analysis were selected stress episodes detected using the information and telecommunication technology of event-related cardiac telemetry (ITT ERCT) presented by the Internet resource “StressMonitor”. Results.For the numerical series of RR-intervals, a clear division into fast and slow components is obtained. An algorithm for identifying the frequency content of heart rate variability has been formulated and tested. A visualization method is proposed that is convenient for comparing data obtained for different patients. A pseudo-phase portrait pattern corresponding to the moment of stress onset is found. The proposed method reduced the discreteness of identifying the stress onset moment from 10 seconds to single heart beats. Conclusion. The correspondence of the results to the verified ITT ERCT method and the Baevsky–Chernikova concept of adaptive risk has been demonstrated. This confirms the possibility of using the time cardiointervalograms smoothing method for the analysis of heart rate variability.

The authors thank S. A. Polevaya, Head of the Department of Psychophysiology, Lobachevsky University of Nizhny Novgorod for the opportunity to use the Internet resource “StressMonitor”.
  1. Bayevsky RM, Ivanov GG. Cardiac rhythm variability: The theoretical aspects and the opportunities of clinical application (lecture). Ultrasonic and Functional Diagnostics. 2001;(3):108–127 (in Russian).
  2. Baevsky RM, Funtova II, Berseneva AP, Chernikova AG, Luchitskaya ES, Prilutsky DA, Semenov YN, Tank J, Slepchenkova IN, Rusanov VB, Bersenev EY, Ivanov GG. Methods and Instruments of Space Cardiology Aboard the International Space Station: Monograph. Moscow: Tekhnosphera; 2016. 368 p. (in Russian).
  3. Baevsky RM, Ivanov GG, Chireikin LV, Gavrilushkin AP, Dovgalevsky PY, Kukushkin YA, Mironova TF, Prilutsky DA, Semenov AV, Fedorov VF, Fleishman AN, Medvedev MM. Analysis of heart rate variability using various electrocardiographic systems (Methodical recommendations, part 1). Journal of Arrhythmology. 2002;(24):65–87 (in Russian).
  4. Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal. 1996;17(3):354–381. DOI: 10.1093/oxfordjournals.eurheartj.a014868.
  5. Goldstein DS, Kopin IJ. Homeostatic systems, biocybernetics, and autonomic neuroscience. Autonomic Neuroscience. 2017;208:15–28. DOI: 10.1016/j.autneu.2017.09.001.
  6. Polevaya SA, Eremin EV, Bulanov NA, Bakhchina АV, Kovalchuk AV, Parin SB. Event-related telemetry of heart rate for personalized remote monitoring of cognitive functions and stress under conditions of everyday activity. Modern Technologies in Medicine. 2019;11(1):109–115. DOI: 10.17691/stm2019.11.1.13.
  7. Ellis RJ, Thayer JF. Music and autonomic nervous system (dys)function. Music Perception. 2010;27(4):317–326. DOI: 10.1525/mp.2010.27.4.317.
  8. Antonets VA, Permiakov SP, Nikulina MV. Applying cardiointervalogram smoothing to analyze heart rate variability. In: Materials of the VII All-Russian Scientific and Practical Conference with International Participation «Heart Rate Variability: Theoretical Aspects and Practical Application in Sports and Mass Physical Culture». 25-26 May 2021, UdSU, Izhevsk. Izhevsk: UdSU; 2021. P. 67–74 (in Russian).
  9. Ryabykina GV, Sobolev AV. Analysis of heart rate variability. Cardiology. 1996;36(10):87–97 (in Russian).
  10. Baevsky RM, Kirillov OI, Kletskin SZ. Mathematical Analysis of Changes in Heart Rate During Stress. Moscow: Nauka; 1984. 224 p. (in Russian).
  11. Shljufman KV, Fishman BE, Frisman EJ. Features of modes for one-dimensional model of ricker. Izvestiya VUZ. Applied Nonlinear Dynamics. 2012;20(2):12–28 (in Russian). DOI: 10.18500/0869-6632-2012-20-2-12-28.
  12. Baevsky RM, Chernikova AG. The problem of the physiological norm: a mathematical functional state model based on analysis of the cardiac rhythm variability. Aerospace and Environmental Medicine. 2002;36(6):11–17 (in Russian).
  13. Baevsky RM, Chernikova AG. A Method for Assessing the Risk of Development of Donosological, Premorbid and Pathological Conditions in a Long-Term Space Flight. Patent No. 2448644 dated 15.09.2010. Assignee: Institute of Biomedical Problems RAS (in Russian).
  14. Parin SB. Humans and animals in extreme situations: Neurochemistry mechanisms, evolutionary aspect. Novosibirsk State University Bulletin. Series: Psychology. 2008;2(2):118–135 (in Russian).
  15. Nekrasova MM, Polevaya SA, Parin SB, Shishalov IS, Bakhchina AV. Method for Determining Stress. Patent No. 2531443 dated 11.11.2013. Assignee: Lobachevsky State University of Nizhny Novgorod (in Russian).
  16. Grigorieva KA, Grigorieva VN, Polevaya SA. Method for Diagnosing Stress in Humans. Patent No. 2624813 dated 11.08.2016. Assignee: Privolzhsky Research Medical University (in Russian).