Abstract
Heart rate variability (HRV) is a widely used measure for the variation of the heartbeat intervals controlled by the autonomic nervous system (ANS), which is primarily obtained by electrocardiogram (ECG) signals. In general, HRV of a healthy person exhibits long-range correlations with dynamic fluctuations, whether the complexity of HRV decreases with aging and incidence of disease. Recently, the complexity of differential inter-beat intervals, referred to differential R-R intervals, is known to be more effective than original R-R intervals to reflect HRV. The multiscale based entropy methods have been developed to quantify HRV using R-R intervals. In spite of their capability, it still remains unreliable quantification of HRV. Here, we propose a new multiscale complexity quantification measure with differential R-R intervals for HRV analysis. To verify the performance of the proposed method, we evaluate the complexity of differential HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The results show that multiscale distribution entropy (MDE) of differential R-R interval has improved capability for quantifying the complexity of HRV regardless of the length of time series.
Highlights
Heart rate variability (HRV) is a measure for the variation of the heartbeat intervals controlled by the autonomic nervous system (ANS) and reflects a number of physiological factors that modulate the normal rhythm of the heart [1]
We present a multiscale complexity measure of HRV using differential R-R interval time series extracted from actual ECG signals, which exploits the advantages of the moving-averaging procedure and distribution entropy (DistEn) for short-term time series analysis
Considering that the multiscale entropy (MSE) and multiscale distribution entropy (MDE) values at time scale 1 correspond sample entropy (SampEn) and DistEn values, the figure shows that DistEn has better performance of complexity analysis for short-term time series compared to SampEn
Summary
Heart rate variability (HRV) is a measure for the variation of the heartbeat intervals controlled by the autonomic nervous system (ANS) and reflects a number of physiological factors that modulate the normal rhythm of the heart [1]. In order to analyze HRV, an electrocardiogram (ECG) signal which is an electrical recording of the heart activity has been mainly used. Using ECG recording, inter-beat (R-R) interval time series is extracted for the complexity analysis of HRV. Various studies have been conducted to analyze the complexity of HRV for the diagnosis of heart failure. According to these studies, while normal heart rhythm represents complex nonlinear dynamics and has long-range correlation
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