Title: Feature extraction and classification of ECG signals with support vector machines and particle swarm optimisation

Authors: Gandham Sreedevi; Bhuma Anuradha

Addresses: Department of Electronics and Communication Engineering, Sri Venkateswara University, Tirupati-517502, India ' Department of Electronics and Communication Engineering, Sri Venkateswara University, Tirupati-517502, India

Abstract: The present work was aimed to present a thorough experimental study that shows the superiority of the generalisation capability of the support vector machine (SVM) approach in the classification of electrocardiogram (ECG) signals. Feature extraction was done using principal component analysis (PCA). Further, a novel classification system based on particle swarm optimisation (PSO) was used to improve the generalisation performance of the SVM classifier. For this purpose, we have optimised the SVM classifier design by searching for the best value of the parameters that tune its discriminant function and upstream by looking for the best subset of features that feed the classifier. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system.

Keywords: electrocardiogram; ECG; principal component analysis; PCA; particle swarm optimisation; PSO; support vector machine; SVM; arrhythmias; classification.

DOI: 10.1504/IJBET.2021.113732

International Journal of Biomedical Engineering and Technology, 2021 Vol.35 No.3, pp.242 - 262

Received: 05 Dec 2017
Accepted: 28 Feb 2018
Published online: 22 Mar 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article