Title: Classification of cardiac arrhythmia using hybrid genetic algorithm optimisation for multi-layer perceptron neural network

Authors: V.S.R. Kumari; P.R. Kumar

Addresses: Department of Electronics and Communication Engineering, Andhra University, Vishakhapatnam, India ' Department of Electronics and Communication Engineering, Andhra University, Vishakhapatnam, India

Abstract: Cardiac arrhythmia indicates the susceptibility of serious heart disease and stroke. Early diagnosis of cardiac arrhythmia helps administering aid to the patients avoiding cardiac complications. An Electrocardiogram (ECG) helps in identifying cardiac arrhythmia. Automated arrhythmia detection was developed in the past few decades attempting to simplify the monitoring task and improve diagnostic efficiencies. ECG arrhythmia detection accuracy improves with the use of machine learning and data mining methods. Several algorithms were developed for the detection and classification of the ECG signals. This study proposes Multi-Layer Perceptron Neural Network (MLPNN) optimisation using Genetic Algorithm (GA) to classify ECG arrhythmia. Symlet is used to extracts R-R intervals from ECG data as features, while symmetric uncertainty assures feature reduction. GA optimises learning rate and momentum. Simulated Annealing (SA) is applied to refine the population of GA.

Keywords: arrhythmia classification; electrocardiograms; R-R interval; MIT-BIH ECG data set; multi-layer perceptron neural networks; GAs; genetic algorithms; simulated annealing; genetic algorithms; optimisation; cardiac arrhythmia; heart disease; feature extraction; strokes.

DOI: 10.1504/IJBET.2016.074199

International Journal of Biomedical Engineering and Technology, 2016 Vol.20 No.2, pp.132 - 149

Received: 09 Feb 2015
Accepted: 29 Jun 2015
Published online: 16 Jan 2016 *

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