Open Access

ARTICLE

Modeling of Heart Rate Variability Using Time-Frequency Representations

by Ghaylen Laouini¹, Ibrahim Mahariq¹, Thabet Abdeljawad^2,3,4,*, Hasan Aksoy⁵

1 College of Engineering and Technology, American University of the Middle East, Kuwait
2 Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
3 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan
4 Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
5 Department of Electrical & Electronic Engineering, University of Turkish Aeronautical Association, Ankara, 06790, Turkey

* Corresponding Author: Thabet Abdeljawad. Email:

(This article belongs to the Special Issue: Advanced signal acquisition and processing for Internet of Medical Things)

Computers, Materials & Continua 2021, 69(1), 1289-1299. https://doi.org/10.32604/cmc.2021.018411

Received 06 March 2021; Accepted 08 April 2021; Issue published 04 June 2021

Abstract

The heart rate variability signal is highly correlated with the respiration even at high workload exercise. It is also known that this phenomenon still exists during increasing exercise. In the current study, we managed to model this correlation during increasing exercise using the time varying integral pulse frequency modulation (TVIPFM) model that relates the mechanical modulation (MM) to the respiration and the cardiac rhythm. This modulation of the autonomic nervous system (ANS) is able to simultaneously decrease sympathetic and increase parasympathetic activity. The TVIPFM model takes into consideration the effect of the increasing exercise test, where the effect of a time-varying threshold on the heart period is studied. Our motivation is to analyze the heart rate variability (HRV) acquired by time varying integral pulse frequency modulation using time frequency representations. The estimated autonomic nervous system (ANS) modulating signal is filtered throughout the respiration using a time varying filtering, during exercise stress testing. And after summing power of the filtered signal, we compare the power of the filtered modulation of the ANS obtained with different time frequency representations: smoothed pseudo Wigner–Ville representation, spectrogram and their reassignments. After that, we used a student t-test to compare the power of heart rate variability in the frequency band of respiration and elsewhere.

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Keywords

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