A comparative approach to ECG feature extraction methods

A comparative approach to ECG feature extraction methods

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 tha...

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Bibliographic Details
Main Authors: Vaneghi, F.M., Oladazimi, M., Shiman, F., Kordi, A., Safari, M.J., Ibrahim, F.
Format: Conference or Workshop Item
Language:English
Published: 2012
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/9269/1/A_comparative_approach_to_ECG_feature_extraction_methods.pdf
http://eprints.um.edu.my/9269/
http://www.scopus.com/inward/record.url?eid=2-s2.0-84859984319&partnerID=40&md5=82f9ec2d1916e0b8b12535a751edbee1 ieeexplore.ieee.org/xpls/absall.jsp?arnumber=6169708
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Institution: Universiti Malaya
Language: English
Description
Summary: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. © 2012 IEEE.

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