Title: Cardiac arrhythmias classification using artificial metaplasticity algorithm

Authors: Yasmine Benchaib; Mohamed Amine Chikh

Addresses: Biomedical Engineering Laboratory, Faculty of Technology, University of Abou Bakr Belkaid, Tlemcen, Algeria ' Biomedical Engineering Laboratory, Faculty of Technology, University of Abou Bakr Belkaid, Tlemcen, Algeria

Abstract: Electrocardiogram (ECG) arrhythmias such as ventricular and atrial arrhythmias are one of the common causes of death. These abnormalities of heart activity may cause immediate death or damage to the heart. If the abnormal symptoms can be detected and diagnosed early, time is saved to prevent the occurrence of heart attack. Therefore, it is necessary to have an effective method for early detection and early treatment. We propose, in this paper, an intelligent method to accurately classify the heartbeat of ECG signals through the Artificial Metaplasticity Multilayer Perceptron (AMMLP). The MIT-BIH database is used to classify arrhythmias into three different types: Premature Ventricular Contraction (PVC), Right Bundle Branch Block (RBBB) and Left Bundle Branch Block (LBBB); normal ECG signals are also used in the study. The obtained AMMLP classification accuracy of 98.25% is an excellent result compared to the classical MLP and recent classification techniques applied to the same database.

Keywords: cardiac arrhythmias; metaplasticity; artificial neural networks; ANNs; intelligent classification; electrocardiograms; ECG signals; heartbeats; multilayer perceptron; MLP; heart rate.

DOI: 10.1504/IJBET.2014.059671

International Journal of Biomedical Engineering and Technology, 2014 Vol.14 No.3, pp.209 - 224

Received: 01 Jul 2013
Accepted: 17 Jan 2014
Published online: 16 Oct 2014 *

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