Title: An automatic ECG arrhythmia diagnosis system using support vector machines optimised with GOA and entropy-based feature selection procedure

Authors: Abdullah Jafari Chashmi; Mehdi Chehel Amirani

Addresses: Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran ' Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran

Abstract: Primary recognition of heart diseases by exploiting computer-aided diagnosis (CAD) machines decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient combination classification model using grasshopper optimisation algorithm (GOA) and support vector machines (SVMs) called GOA-SVM for ECG arrhythmia diagnosis is proposed. In this approach, the combination of discrete wavelet transform and higher-order statistics is used to feature extraction and the entropy-based feature selection method. The proposed method has been compared with PSO-SVMs and SVM-RBF kernel function for classifying the five classes of heartbeat categories. Our proposed system is able to classify the arrhythmia classes with high accuracy (99.66%). The simulation results show that classification accuracy in SVM-GOA method is better than SVM-RBF and neural network classifier.

Keywords: ECG; classification; entropy; grasshopper optimisation algorithm; GOA; higher order statistics.

DOI: 10.1504/IJMEI.2022.119309

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.1, pp.52 - 62

Received: 17 Sep 2019
Accepted: 02 May 2020
Published online: 01 Dec 2021 *

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