Title: ECG signals classification: a review

Authors: Essam H. Houssein; Moataz Kilany; Aboul Ella Hassanien

Addresses: Faculty of Computers and Information, Minia University, Egypt ' Faculty of Computers and Information, Minia University, Egypt ' Faculty of Computers and Information, Cairo University, Egypt

Abstract: Electrocardiogram (ECG), non-stationary signals, is extensively used to evaluate the rate and tuning of heartbeats. The main purpose of this paper is to provide an overview of utilizing machine learning and swarm optimization algorithms in ECG classification. Furthermore, feature extraction is the main stage in ECG classification to find a set of relevant features that can attain the best accuracy. Swarm optimization algorithm is combined with classifiers for the purpose of searching the best value of classification parameters that best fits its discriminant purpose. Finally, this paper introduces an ECG heartbeat classification approach based on the water wave optimization (WWO) and support vector machine (SVM). Published literature presented in this paper indicates the potential of ANN and SVM as a useful tool for ECG classification. Author strongly believes that this review will be quite useful to the researchers, scientific engineers working in this area to find out the relevant references.

Keywords: electrocardiogram; ECG; feature extraction; feature optimisation; classification; artificial neural networks; ANNs; support vector machines (SVMs).

DOI: 10.1504/IJIEI.2017.087944

International Journal of Intelligent Engineering Informatics, 2017 Vol.5 No.4, pp.376 - 396

Received: 14 Dec 2016
Accepted: 02 Apr 2017
Published online: 13 Nov 2017 *

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