The application of wavelet and feature vectors to ECG signals

Mirjam Jonkman, A Matsuyama

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)


    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. Copyright � 2006 ACPSEM/EA.
    Original languageEnglish
    Pages (from-to)13-17
    Number of pages5
    JournalAustralasian Physical and Engineering Sciences in Medicine
    Issue number1
    Publication statusPublished - 2006

    Bibliographical note

    Previously published as paper in Proceedings from TENCON 2005 - 2005 IEEE Region 10 Conference.


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