NDLI: An approach for ECG classification based on wavelet feature extraction and decision tree

Content Provider	IEEE Xplore Digital Library
Author	Leigang Zhang Hu Peng Chenglong Yu
Copyright Year	2010
Description	Author affiliation: Dept. of Electronic Science &, Technology, University of Science & Technology of, China, Hefei, China (Leigang Zhang; Hu Peng; Chenglong Yu)
Abstract	Automatic analysis of cardiac arrhythmias is very important for diagnosis of cardiac abnormities. This paper presents a novel approach that classifies ECG signals with the combination of Wavelet transform and Decision tree classification. This approach has two aspects. In the first aspect, we utilize the wavelet transform to extract the ECG signals wavelet coefficients as the first features and utilize the combination of principal component analysis (PCA) and independent component analysis (ICA) to remove the first features relativity and search this features independence as the new features, then we add the RR interval as the final features. In the second aspect, we utilize the ID3 algorithm which is one of analysis decision tree methods as the classifier to recognize the different heartbeat arrhythmias. We utilize the MIT-BIH Arrhythmia Database to create the classification and test the classification. The results confirm its high reliability and high accuracy is very well.
Starting Page	1
Ending Page	4
File Size	477955
Page Count	4
File Format	PDF
ISBN	9781424475568
e-ISBN	9781424475551
e-ISBN	9781424475544
DOI	10.1109/WCSP.2010.5633782
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Classification Electrocardiography PCA-ICA Feature extraction ID3 Classification algorithms Discrete wavelet transforms ECG signal Wavelet coefficient Classification tree analysis Principal component analysis
Content Type	Text
Resource Type	Article

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