Open AccessFeature Selection for ECG Beat Classification using Genetic Algorithms
Author(s) -
Çağla Sarvan,
Nalan Özkurt,
Korhan Karabulut
Publication year - 2018
Publication title -
akıllı sistemler ve uygulamaları dergisi
Language(s) - English
Resource type - Journals
ISSN - 2667-6893
DOI - 10.54856/jiswa.201812045
Subject(s) - pattern recognition (psychology) , discrete wavelet transform , feature selection , wavelet , artificial intelligence , wavelet transform , entropy (arrow of time) , feature (linguistics) , standard deviation , beat (acoustics) , genetic algorithm , mathematics , algorithm , computer science , statistics , machine learning , linguistics , philosophy , physics , quantum mechanics , acoustics
In this study, genetic algorithm method was used to select the most suitable set of features for classification of arrhythmia types of heart beats. Normal, right branch block, left branch block and pace rhythm samples of electrocardiography (ECG) signals which obtained from the MIT-BIH cardiac arrhythmia database were used in the classification. Mean, standard deviation, energy and entropy of discrete wavelet transform (DWT) coefficients were proposed as the features for the classification. By using the proposed DWT method, 16 features which have high classification accuracy were obtained among the 208 feature sets constructed from 13 different wavelet types by applying the genetic algorithm method. It was observed that the features that increase accuracy can be detected by the genetic algorithm and the feature set obtained from the coefficients of the different types of wavelets selected at different levels show higher performance than the coefficients obtained from the standard individual wavelet in the ECG arrhythmia classification.