Charles Darwin University

CDU eSpace
Institutional Repository

CDU Staff and Student only

The Application of Wavelet and Feature Vectors to ECG signals

Matsuyama, Aya and Jonkman, Mirijam (2005). The Application of Wavelet and Feature Vectors to ECG signals. In: Tencon 2005 IEEE Region 10, Melbourne, Australia, 21-24 November 2005.

Document type: Conference Paper
Citation counts: Google Scholar Search Google Scholar

Author Matsuyama, Aya
Jonkman, Mirijam
Title The Application of Wavelet and Feature Vectors to ECG signals
Conference Name Tencon 2005 IEEE Region 10
Conference Location Melbourne, Australia
Conference Dates 21-24 November 2005
Conference Publication Title Proceedings Tencon 2005 IEEE Region 10
Place of Publication New Zealand
Publisher IEEE
Publication Year 2005
Volume Number 1
ISBN 0-7803-9312-0   (check CDU catalogue open catalogue search in new window)
Total Pages 4
HERDC Category E1 - Conference Publication (DEST)
Abstract The Electrocardiogram (ECG) is one of the most commonly known biological signals, which are traditionally analyzed in the time-domain by skilled physicians. However, pathological conditions may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, Arrhythmia ECG signals were examined. There were two stages in analyzing ECG signals: feature extraction and feature classification. To extract features from ECG signals, wavelet decomposition was first applied and feature vectors of normalized energy and entropy were constructed. Vector quantisation technique was applied to these feature vectors to classify signals. The results showed that Normal Sinus Rhythm ECGs and Arrhythmia ECGs composed different clusters.
Additional Notes 10.1109/TENCON.2005.300875
Description for Link Link to conference paper
Version Filter Type
Access Statistics: 163 Abstract Views  -  Detailed Statistics
Created: Fri, 12 Sep 2008, 08:35:25 CST by Administrator