Title :
A Comparative Approach to ECG Feature Extraction Methods
Author :
Vaneghi, Fatemeh Molaei ; Oladazimi, Maysam ; Shiman, F. ; Kordi, Afshan ; Safari, M.J. ; Ibrahim, F.
Author_Institution :
Dept. of Biomed. Eng., Univ. Malaya, Kuala Lumpur, Malaysia
Abstract :
This paper discusses six most frequent methods used to extract different features in Electrocardiograph (ECG) signals namely Autoregressive (AR), Wavelet Transform (WT), Eigenvector, Fast Fourier Transform (FFT), Linear Prediction (LP), and Independent Component Analysis (ICA). The study reveals that Eigenvector method gives better performance in frequency domain for the ECG feature extraction.
Keywords :
autoregressive processes; eigenvalues and eigenfunctions; electrocardiography; fast Fourier transforms; feature extraction; independent component analysis; medical signal processing; wavelet transforms; ECG feature extraction methods; ECG signals; autoregressive method; eigenvector method; electrocardiograph signals; fast Fourier transform method; independent component analysis; linear prediction method; wavelet transform method; Electrocardiography; Feature extraction; Noise; Time frequency analysis; Wavelet transforms; AR; ECG feature extraction; Eigenvector; FFT; ICA; LP; Wavelet;
Conference_Titel :
Intelligent Systems, Modelling and Simulation (ISMS), 2012 Third International Conference on
Conference_Location :
Kota Kinabalu
Print_ISBN :
978-1-4673-0886-1
DOI :
10.1109/ISMS.2012.35
