We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Study of ECG feature extraction for automatic classification based on wavelet transform

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

1 Author(s)
Ge Dingfei ; Sch. of Inf. & Electron. Eng., Zhejiang Univ. of Sci. & Technol., Hangzhou, China

Electrocardiogram (ECG) feature extraction plays an important role in automatic classification and diagnosis. The current study focuses on the feature extraction of premature ventricular contraction (PVC) and normal sinus rhythm (NSR) for the discrimination purpose between them. The data in the analysis were collected from MIT-BIH database. A beat detection algorithm that was not affected by beat shape was introduced in the study. The ECG features were extracted based on wavelet transform for the analysis. Two feature sets were formed by selected wavelet coefficients and statistic parameters of wavelet coefficients for the comparative study. Support Vector Machine (SVM) algorithm was utilized to classify the ECG beats. The experimental results show that it is possible and feasible to extract ECG features with lower dimensions from wavelet coefficients in order to improve the classification results.

Published in:

Computer Science & Education (ICCSE), 2012 7th International Conference on

Date of Conference:

14-17 July 2012