Title
An approach for ECG classification based on wavelet feature extraction and decision tree
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.
Year
DOI
Venue
2010
10.1109/WCSP.2010.5633782
WCSP
Keywords
Field
DocType
pca-ica,electrocardiography,ecg classification,wavelet transforms,ecg signal,wavelet coefficient,wavelet feature extraction,medical signal processing,cardiac arrhythmias,independent component analysis,id3 algorithm,feature extraction,pca,signal classification,heartbeat arrhythmias,wavelet transform,classification,decision tree classification,ica,id3,principal component analysis,decision trees,accuracy,decision tree,classification algorithms
Decision tree,Pattern recognition,Computer science,Feature extraction,Independent component analysis,Artificial intelligence,Statistical classification,ID3 algorithm,Principal component analysis,Wavelet,Wavelet transform
Conference
Volume
Issue
ISSN
null
null
null
ISBN
Citations 
PageRank 
978-1-4244-7554-4
10
0.62
References 
Authors
4
3
Name
Order
Citations
PageRank
Leigang Zhang1100.62
Hu Peng24613.63
Chenglong Yu3101.30