By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Leigang Zhang ; Dept. of Electron. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China ; Hu Peng ; Chenglong Yu

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.

Published in:

Wireless Communications and Signal Processing (WCSP), 2010 International Conference on

Date of Conference:

21-23 Oct. 2010