The chaotic characteristics detection based on multifractal detrended fluctuation analysis of the elderly 12-lead ECG signals

Dezhao Jiao, Zikuan Wang, Jin Li, Feilong Feng and Fengzhen Hou

Physica A: Statistical Mechanics and its Applications, 2020, vol. 540, issue C

Abstract: ECG analysis is an important method of heart disease diagnosis. During the diagnostic process,many signal characteristics are hidden in the 12-lead ECG. To research these characteristics and improve diagnostic efficiency, it is very urgent to study the 12-lead ECG signal. In this paper, we used multifractal detrended fluctuation analysis(MFDFA) method to detect chaotic characteristics of three sets of signals, which is generated from Myocardial Infarction(MI) state, Arrhythmia state and healthy state. Calculating and analyzing the Hurst exponent, the mass exponent and the multifractal spectrum, we found that the three kinds of signals have different long-range correlation and multifractal characteristics. The result shows that the method could robustly identify patterns generated from the healthy and pathologic state. These results will assist in the intensive study of cardiac signals, guide the analysis of physiological states and provide a reference for clinical diagnosis and treatment.

Keywords: 12-lead ECG signals; Chaotic characteristics; MFDFA; Hurst exponent; Mass exponent; Multifractal spectrum (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119318163

DOI: 10.1016/j.physa.2019.123234

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