Wavelets as methods for ECG signal processing.
Mariana Moga, Victor Dan Moga, C Luca, Gh. Mihalas · PubMed · 2004
The analysis of very rapid fluctuations in heart rate (expressed as heart rate variability) is hampered by the fact that the cardiac events are not regularly spaced in time. However, if we are interested in rapid fluctuations theoretically the unit of time mentioned above should be as small as possible. The analysis of fluctuation in heart rate has become increasingly important both in physiological studies and in modeling of the neurocardiovascular system. The big disadvantage of a Fourier analysis however is that it has only frequency resolution and no time resolution. The aim of our study is to prove the value of wavelet transform for the analysis of ECG like signals. This means that although we might be able to determine all the frequencies present in a signal, we do not know when they are present. The idea behind these time-frequency joint representations is to cut the signal of interest into several parts and then analyze the parts separately. The result will be a collection of time-frequency representations of the signal, all with different resolutions. Because of this collection of representations we can speak of multiresolution analysis. In this case, we normally do not speak about time-frequency representation but about time - scale representations, scale being in a way the opposite of frequency, because the term frequency is reserved for the Fourier transform. Using wavelet transform for the analysis of ECG signals we noticed the chance to could operate with a new type of classification of the components of the signal ECG that probably will offer new data on the ECG analysis.