The Application of Wavelet and Feature Vectors to ECG Signals

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.