The wavelet transform is well suited for multiresolution signal decomposition, offering an effective sub-band coding technique for the compression and reconstruction of ECG data.
The wavelet transform represents a promising and relevant tool for the analysis and compression of physiological signals such as the ECG.
The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.
Crowe et al. (Fri,) conducted a other in ECG and heart rate variability. Wavelet transform was evaluated on ECG data compression and reconstruction. The wavelet transform is well suited for multiresolution signal decomposition, offering an effective sub-band coding technique for the compression and reconstruction of ECG data.