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The application of wavelet and feature vectors to ECG signals

Matsuyama, A. and Jonkman, Mirjam (2006). The application of wavelet and feature vectors to ECG signals. Australasian Physical and Engineering Sciences in Medicine,29(1):13-17.

Document type: Journal Article
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IRMA ID 77514990xPUB14
Title The application of wavelet and feature vectors to ECG signals
Author Matsuyama, A.
Jonkman, Mirjam
Journal Name Australasian Physical and Engineering Sciences in Medicine
Publication Date 2006
Volume Number 29
Issue Number 1
ISSN 0158-9938   (check CDU catalogue open catalogue search in new window)
Start Page 13
End Page 17
Total Pages 5
Place of Publication SA, Australia
Publisher Australasian College of Physical Scientists and Engineers in Medicine
Field of Research 0903 - Biomedical Engineering
HERDC Category C1 - Journal Article (DEST)
Abstract The Electrocardiogram (ECG) is one of the most commonly known biological signals. Traditionally ECG recordings are analysed in the time-domain by skilled physicians. However, pathological conditions may not always be obvious in the original time-domain signal. Fourier analysis provides frequency information but has the disadvantage that time characteristics will be lost. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. Here a new method, the combination of wavelet analysis and feature vectors, is applied with the intent to investigate its suitability as a diagnostic tool. ECG signals with normal and abnormal beats were examined. There were two stages in analysing ECG signals: feature extraction and feature classification. To extract features from ECG signals, wavelet decomposition was first applied and feature vectors of normalised energy and entropy were constructed. These feature vectors were used to classify signals. The results showed that normal beats and abnormal beats composed different clusters in most cases. In conclusion, the combination of wavelet transform and feature vectors has shown potential in detecting abnormalities in an ECG recording. It was also found that normalised energy and entropy are features, which are suitable for classification of ECG signals.
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Created: Fri, 12 Sep 2008, 08:35:25 CST by Administrator