Exploiting Interchannel Correlations in Multichannel Compressed Sensing ECG Systems

Multichannel elctrocardiogram (MECG) signals are correlated both in spatial domain as well as in temporal domain and this correlation is stronger at multiscale levels (Fig. 1). To exploit this correlation in compressed sensing (CS) based ECG tele-monitoring systems, a joint multiscale compressed sensing (JMCS) technique is proposed in this work. CS is a novel signal acquisition/reconstruction paradigm that is proposed for addressing the power efficiency and complexity issues in wireless body area network (WBAN) enabled ECG tele-monitoring systems. Here, JMCS is proposed to apply on jointly sparse subband signals from each channel instead of time domain MECG. Since subband signals at each wavelet scale are more correlated, they share strong common support information and hence posses higher joint sparsity than MECG signals in time domain. Joint acquisition and reconstruction of high frequency subband signals is formulated as a multi-measurement vector (MMV) problem. To exploit the subband dependencies during the joint reconstruction a sparse Bayesian learning (SBL) based algorithm is employed which is known to be very efficient for finding joint sparse solution. Significant performance gain in terms of diagnostic quality of reconstructed MECG is achieved at reduced number of measurements which directly translates into higher compression efficiency of the CS based WBAN systems.