THE USAGE OF STATISTICAL FEATURES IN THE APPROXIMATION COMPONENTS OF WAVELET DECOMPOSITION FOR ECG CLASSIFICATION: A CASE STUDY FOR STANDING, WALKING AND SINGLE JUMP CONDITIONS

Yıl 2018, Cilt: 8 Sayı: 2, 178 - 182, 30.11.2018

Makbule Hilal Mütevelli , Semih Ergin

Öz

The purpose of this study is to classify
electrocardiogram (ECG) signals with a high accuracy rate. The ECG signals used
are obtained from the Physiobank archive. These signals are preprocessed to
remove noise. Features with distinctiveness in classification are obtained both
in the time domain and the frequency domain. The Discrete Wavelet Transform
method is used for feature extraction in frequency domain. ECG signals are
classified by the Naive Bayes method after the required features are extracted.

Anahtar Kelimeler

ECG, statistical feature extraction, discrete wavelet transform, ECG classification

Kaynakça

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