Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar, M. and Pachori, R.B. and Acharya, U.R. (2017) Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework. Entropy, 19 (9). p. 488. ISSN 1099-4300

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Official URL: http://dx.doi.org/10.3390/e19090488

Abstract

Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.

Item Type: Article
Uncontrolled Keywords: Myocardial infarction (MI); electrocardiogram (ECG) beats; flexible analytic wavelet transform (FAWT); sample entropy; classification
Subjects: R Medicine
Divisions: Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 06 Sep 2018 01:27
Last Modified: 06 Sep 2018 01:27
URI: http://eprints.um.edu.my/id/eprint/19134

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