A complexity measure for heart rate signals

Jiayi He, Pengjian Shang and Jing Wang

Physica A: Statistical Mechanics and its Applications, 2019, vol. 533, issue C

Abstract: In this paper, we present an adaptive measurement to quantify the complexity of time series, called q-sample entropy difference mean (qSEDiffAve), and apply this method to heart rate time series. It quantifies the structural information of the signals and helps identify different conditions of the heart systems. Experiments show that compared with the traditional sample entropy (SampEn), qSEDiffAve has significant advantages in reflecting the inherent information of sequences. It also reveals that the qSEDiffAve curves of healthy subjects are significantly different from the atrial fibrillation (AF) and congestive heart failure (CHF) groups, because the CHF signals tend to fluctuate between narrow ranges while the atrial fibrillation (AF) signals are random, and the signals of healthy subjects are usually mixed by signals with different dynamic characteristics, showing higher complexity. In addition, the most important parameter in the model, q, can enlarge the differences between qSEDiffAve curves of healthy and pathological states.

Keywords: Complexity; Irregularity; Heart rate; Physiological signals (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:533:y:2019:i:c:s0378437119311987

DOI: 10.1016/j.physa.2019.122054

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