The compression of electrocardiogram(ECG) data is of great practical significance and received much attention. The importance of ECG data compression has become evident in many aspects including: a) increased storage capability of ECG data for the improvement of the functionality of ambulatory ECG monitors and Hoters, b) lower transmission bit rate of off-line ECG's over public phone lines to a remote interpretation center for real-time ECG's. Much research work of ECG compression has been done for the past years. Compression is accomplished by detecting and eliminating redundancy in the given information from the ECG signal. Most ECG compression algorithms belong to either of the following categories: a) direct data compression methods, which detect redundancies by direct analysis of actual signal samples, b) transform methods, which mainly utilize spectral and energy distribution analysis for detecting redundancies. The wavelet-based compression algorithms presented in this paper fall in the latter category.
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