Computational complexity and power consumption are prominent issues in wireless telemonitoring applications involving physiological signals. Because of its energy-efficient data reduction procedure, compressed sensing (CS) emerged as a promising framework to address these challenges. In this work, a multi-channel CS framework is explored for multi-channel electrocardiogram (MECG) signals. The work focuses on the successful joint recovery of the MECG signals using a low number of measurements by exploiting the correlated information across the channels. A CS recovery algorithm based on weighted mixed-norm minimization (WMNM) is proposed that exploits the joint sparsity of MECG signals in the wavelet domain and recovers signals from all the channels simultaneously. The proposed WMNM algorithm follows a weighting strategy to emphasize the diagnostically important MECG features. Experimental results on various MECG databases show that the proposed method can achieve superior reconstruction quality with high compression efficiency as compared to its non-weighted counterpart and other existing CS-based ECG compression techniques. 
Weighted mixed-norm minimization based joint compressed sensing recovery of multi-channel electrocardiogram signals