Multiscale entropy algorithms and their applications in cardiac disease discrimination

Wan, Xiangkui and Zhu, Binru and Jin, Zhiyao and Zhang, Mingru and Li, Yan (2020) Multiscale entropy algorithms and their applications in cardiac disease discrimination. Journal of Mechanics in Medicine and Biology, 20 (8):2050052. pp. 1-13. ISSN 0219-5194


Abstract

In recent years, the number of cardiac disease patients has been increasing. Modern medical research has shown that the complexity of electrocardiogram (ECG) signals is related to cardiovascular diseases. This paper investigates the difference in complexity of ECG data from the people with different cardiovascular diseases, such as atrial fibrillation (AF), ventricular arrhythmia (VA) and congestive heart failure (CHF). The empirical mode decomposition (EMD) and multiscale entropy method are used to analyze the ECG data, and a mathematical model established by a support vector machine is used to identify different diseases. The accuracy recognition rate of the AF recognition is 96.25%, and that of the CHF and VA reach 90.26% and 92.20%, respectively. The experimental results show that the recognition method proposed in this paper is successful.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - No Department (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - No Department (1 Jul 2013 -)
Date Deposited: 29 Jan 2021 04:00
Last Modified: 31 Jan 2021 22:57
Uncontrolled Keywords: ECG, RR interval, multiscale entropy
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
Identification Number or DOI: https://doi.org/10.1142/S0219519420500529
URI: http://eprints.usq.edu.au/id/eprint/40969

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