Classification of ECG signal using Complex-Valued Continuous Wavelet Transforms

Abstract

In this study the complex-valued continuous wavelet transform (CWT) has been applied in detection of Electrocardiograms (ECG) as response to various signal classification methods such as Fourier transforms and other tools of time frequency analysis. Experiments have shown that CWT may serve as a detector of non-stationary signal changes as ECG The tested signal is corrupted by short time events. We applied CWT to detect short-time event and the result image representation of the signal has showed us that one can easily find the discontinuity at the time scale representation. Analysis of ECG signal using complex-valued continuous wavelet transform is the first step to detect possible changes and alternans. In the second step, modulus and phase must be thoroughly examined. Thus, maxima, minima, or even inflection points are found as detectable maxima or minima. We have proved that the Complex-Valued CWT can be used as a good detector in nonstationary signals such as ECG.

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