Low-complexity compressed sensing with variable orthogonal multi-matching pursuit and partially known support for ECG signals

In this paper, we present low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN). We first exploit ECG properties in the wavelet domain to extend the partially known support set (PKS) so as to reduce the support augmentation and estimation efforts in the iterative recovery algorithm. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm is proposed, which uses orthogonal matching pursuit (OMP) algorithm in the first phase to effectively augment the support set with reliable supports and adopts the orthogonal multi-matching pursuit (OMMP) in the second phase to rescue the missing supports. The reconstruction performance is thus enhanced. Furthermore, the computation-intensive pseudo-inverse operation for signal reconstruction is simplified by the matrix-inversion-free technique based on QR decomposition. The performance and complexity comparisons manifest the advantages of our proposed techniques.