Block-Sparsity Based Compressed Sensing for Multichannel ECG Reconstruction.

Multichannel electrocardiogram (MECG) provides significant information for the detection of cardiovascular diseases. Compressed sensing (CS) is a simultaneous sensing and reconstruction technique from a few compressed measurements with low level of distortion. CS promises to lower energy consumption of sensing nodes for wireless body area network (WBAN) in continuous ECG monitoring. In this paper, we propose an energy efficient novel block-sparsity based compressed sensing for MECG reconstruction which exploits both spatial and temporal correlations in the wavelet domain effectively. Experimental results show that the proposed method achieve MECG data compression and reconstruction better than others.