Feature Selection for ECG Beat Classification using Genetic Algorithms with A Multi-objective Approach

Abstract

To identify appropriate features in classification studies is a common problem in many areas. In this study a genetic algorithm method with multi-objective approach is proposed for selecting the features that give high performance ratio in classifying cardiac arrhythmia. Discrete Wavelet Transform (DWT) were used for extracting features from Normal right bundle branch block left bundle branch block and paced rhythm recordings of electrocardiography (ECG) signals which were taken from the MIT-BIH cardiac arrhythmia database. Using 13 different wavelet types 208 features were obtained by the DWT method. Among these features a minimum number of feature sets were chosen to provide high performance in classification. Then the classification results were compared with the results of the classical genetic algorithm which aims to improve accuracy.