Classification of cardiac arrhythmia using hybrid genetic algorithm optimisation for multi-layer perceptron neural network Online publication date: Sat, 16-Jan-2016
by V.S.R. Kumari; P.R. Kumar
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 20, No. 2, 2016
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.
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