Title: Feature Selection for ECG Beat Classification using Genetic Algorithms

Abstract: 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.

Keywords: ECG heart beat classification; arrythmia; discrete wavelet transform; wavelet features; feature selection; artificial neural networks; genetic algorithm

Başlık: Genetik Algoritmalar Kullanarak EKG Vuru Sınıflandırması için Öznitelik Seçimi

Özet: Bu çalışmada, kalp vurularının aritmi tipine göre sınıflandırılmasında en uygun öznitelik setinin seçilmesi için genetik algoritma yöntemi kullanılmıştır. Sınıflandırmada MIT-BIH kalp aritmi veri tabanından elde edilen elektrokardiyografi (EKG) sinyallerinin normal, sağ dal bloku, sol dal bloku ve pace ritmi örnekleri kullanılmıştır. Yaygın kullanımı olan kesikli dalgacık dönüşümü (KDD) katsayılarının ortalaması, standart sapması, enerjisi ve entropisi sınıflandırmada kullanılabilecek öznitelik ana kümesi olarak önerilmiştir. Önerilen KDD yöntemi ile 13 farklı dalgacık tipi için elde edilen 208 adetlik öznitelik seti içerisinden yüksek sınıflandırma doğruluğu sağlayan 16 adetlik öznitelik seti genetik algoritma yöntemi uygulanarak elde edilmiştir. Genetik algoritma yöntemi ile sınıflandırma doğruluğunu yükselten özniteliklerin tespit edilebildiği ve farklı tip dalgacıkların farklı seviyelerde seçilen katsayılarından elde edilen öznitelik setinin standart olan tek tip dalgacıktan elde edilen katsayılara göre EKG aritmi sınıflandırmasında daha yüksek başarım gösterdiği gözlemlenmiştir.

Anahtar kelimeler: EKG vuru sınıflandırması; aritmi; kesikli dalgacık dönüşümü; dalgacık öznitelikleri; öznitelik seçimi; yapay sinir ağları; genetik algoritmalar

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