Fuzzy logic approach for heart rate variability

  • Alexander Martynenko D. Sc., Professor, Department of Hygiene and Social Medicine, V. N. Karazin Kharkiv National University https://orcid.org/0000-0002-0609-2220
  • Gianfranco Raimondi MD, PhD, Prof., Sapienza University of Rome
  • Nikita Budreiko Assistant, Department of hygiene and social medicine, V. N. Karazin Kharkiv National University School of Medicine
  • Liudmila Maliarova Assistant, Department of hygiene and social medicine, V. N. Karazin Kharkiv National University School of Medicine
Keywords: heart rate variability, fuzzy logic

Abstract

Intrioution. The heart rate variability (HRV) is based on measuring (time) intervals between R-peaks (of RR-intervals) of an electrocardiogram (ECG) and plotting a rhythmogram on their basis with its subsequent analysis by various mathematical methods that are classified as Time Domain (TD), Frequency Domain (FD) and Nonlinear (NM) [1, 2]. Diversity of methods and approaches to analysis of HRV is stemming from complexity and nonlinearity of the phenomenon itself, as well as from greater diversity of physiological reactions of an organism, both in normal and pathological states. Therefore, it appears relevant and important to incorporate currently existing HRV indicators and norms into a unified Fuzzy Logic (FL) methodology, which in turn will allow to integrally assess each metric and HRV results as a whole. Objective. We propose a Fuzzy Logic algorithm for incorporating into a single view of each metric, – Time Domain, Frequency Domain, Nonlinear Methods and HRV as a whole. Materials and methods. We define by FL the extent of belonging to normal state both for each distinct HRV metric – TD, FD and NM, and for a patient's HRV in general. Membership functions of any HRV index and defuzzification rules for FL scores was defined. In order to implement the proposed algorithm, specified parameters of mean values of HRV (М) indicators and their standard deviation (σ) have been found in scientific publications on HRV [1, 3, 7, 8, 9, 10]. We use for FL algorithm demonstration a long-term HRV records by Massachusetts Institute of Technology - Boston’s Beth Israel Hospital (MIT-BIH) from [11], a free-access, on-line archive of physiological signals for Normal Sinus Rhythm (NSR) RR Interval, Congestive Heart Failure (CHF) RR Interval and Atrial Fibrillation (AF) Databases [12]. Conclusion. In this article, we have presented a comprehensive view of HRV by Fuzzy Logic technology and thoroughly examined the peculiarities of its application and interpretation. Of all considered examples of FL analysis, the worst result is demonstrated by a patient from the AF group, while the best one belongs to a patient from the NSR group. Difference in FL Scores between these patients from NSR and CHF groups is almost 4 times, while between patients from NSR and АF groups it is almost 6 times. It appears especially important to implement such a design in portable medical devices for quick and easy interpretation of numerous parameters measured by them. 

Downloads

Download data is not yet available.

Author Biographies

Alexander Martynenko, D. Sc., Professor, Department of Hygiene and Social Medicine, V. N. Karazin Kharkiv National University

6, Svobody sq., Kharkiv, Ukraine, 61022

Gianfranco Raimondi, MD, PhD, Prof., Sapienza University of Rome

5, Piazzale Aldo Moro, Rome, Italy, 00185

Nikita Budreiko, Assistant, Department of hygiene and social medicine, V. N. Karazin Kharkiv National University School of Medicine

6, Svobody sq., Kharkiv, Ukraine, 61022

Liudmila Maliarova, Assistant, Department of hygiene and social medicine, V. N. Karazin Kharkiv National University School of Medicine

6, Svobody sq., Kharkiv, Ukraine, 61022

References

Task force of the European society of cardiology and the North American society of pacing and electrophysiology. Heart rate variability – standards of measurement, physiological interpretation, and clinical use. (1996). Circulation, 1996. vol. 93, iss. 5, pp.1043–1065.

Yabluchansky N., Martynenko A. (2010). Heart Rate Variability for clinical practice. 2010. Kharkiv, Univer. Press, 131 p. (in Russ.) depositary: http://dspace.univer.kharkov.ua/handle/ 123456789/1462

Voss A, Schroeder R, Heitmann A, Peters A, Perz S. (2015) Short-Term Heart Rate Variability—Influence of Gender and Age in Healthy Subjects. PLoS ONE. 2015; 10 (3): e0118308

Shafler F., Ginsberg J.P. (2017). An Overview of Heart Rate variability Metrics and Norms // Frontiers in Public Health, v. 5, art. 258, p.1–17. https://doi.org/10.3389/fpubh.2017.00258

Dogan I. An Overview of Soft Computing. Procedia Computer Science. 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, Vienna, Austria. 102: 34–38 https://doi.org/10.1016/j.procs.2016.09.366

Yanase J., Triantaphyllou E. (2019). A Systematic Survey of Computer-Aided Diagnosis in Medicine: Past and Present Developments // Expert Systems with Applications. 138:112821. https://doi.org/10.1016/j.eswa.2019.112821

Martynenko A, Raimondi G, Budreiko N. Time Irreversibility and Complexity of Heart Rate Variability. Journal of V. N. Karazin` KhNU Series «Medicine». 2021; 41: 5–15. https://doi.org/10.26565/2313-6693-2021-41-01

Nunan D., Sandercock G., Brodie D. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing Clin Electrophysiol. 2010; 33: 1407–1417.

Umetani K, Singer DH, McCraty R, Atkinson M. (1998). Twenty-four hour time domain heart rate variability and heart rate: relations to age and gender over nine decades. J Am Coll Cardiol. 1998

Nastanova z kardiologii (2009). Red. Kovalenko V.M., Кyiv, МОРІОН, 2009,1368 p. [in Ukrainian]

Goldberger, A.L. et al. (2000). Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation, 2000, 101, 215–220.

Moody GB, Mark RG. (1983). A new method for detecting atrial fibrillation using R-R intervals. Computers in Cardiology, 1983, 10: 227–230.

Published
2021-06-30
How to Cite
Martynenko, A., Raimondi, G., Budreiko, N., & Maliarova, L. (2021). Fuzzy logic approach for heart rate variability. The Journal of V. N. Karazin Kharkiv National University, Series "Medicine", (42). https://doi.org/10.26565/2313-6693-2021-42-01