A Comparative Approach to ECG Feature Extraction Methods

Molaei-Vaneghi, F; Oladazimi, M; Shiman, F; Kordi, A; Safari, MJ; Ibrahim, F

doi:10.1109/ISMS.2012.35

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A Comparative Approach to ECG Feature Extraction Methods

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6169708
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Molaei-Vaneghi, F., Oladazimi, M., Shiman, F., Kordi, A., Safari, M., & Ibrahim, F. (2012). A Comparative Approach to ECG Feature Extraction Methods. In 2012 Third International Conference on Intelligent Systems Modelling and Simulation (pp. 252-256). Piscataway, NJ, USA: IEEE.

Cite as: https://hdl.handle.net/21.11116/0000-0001-8F60-2

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.