PMID: 16623217Apr 21, 2006Paper

The application of wavelet and feature vectors to ECG signals

Australasian Physical & Engineering Sciences in Medicine
A Matsuyama, M Jonkman

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 ...Continue Reading

References

May 1, 1991·Medical & Biological Engineering & Computing·P WindygaJ L Coatrieux
Sep 1, 1991·Acta Neurologica Scandinavica·V MartinelliN Canal
Jun 1, 1997·International Journal of Medical Informatics·I Provazník, J Kozumplík
Jan 13, 2004·Journal of Electrocardiology·Madalena CostaAry L Goldberger

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Citations

Aug 20, 2016·Australasian Physical & Engineering Sciences in Medicine·D Panigrahy, P K Sahu

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