Sparse decompression is a new theory for signal processing, having the advantage in that the base (dictionary) used in this theory is over-complete, and can reflect the nature of signa1. So the sparse decompression of signal can get sparse representation, which is very important in data compression. In this paper, a novel ECG compression method for multi-channel ECG signals was introduced based on the Simultaneous Orthogonal Matching Pursuit (S-OMP). The proposed method decomposes multi-channel ECG signals simultaneously into different linear expansions of the same atoms that are selected from a redundant dictionary, which is constructed by Hermite fuctions and Gobar functions in order to the best match the characteristic of the ECG waveform. Compression performance has been tested using a subset of multi-channel ECG records from the St.-Petersburg Institute of Cardiological Technics database, the results demonstrate that much less atoms are selected to present signals and the compression ratio of Multi-channel ECG can achieve better performance in comparison to Simultaneous Matching Pursuit (SMP).
Wavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure