Comparison of four recovery algorithms used in compressed sensing for ECG signal processing

Compressed Sensing (CS) has been used in ECG signal compressing with the rapid development of real-time & dynamic ECG applications. Signal reconstruction process is an essential step in CS-based ECG processing. Many recovery algorithms have been reported in the last decades. However, the comparative study on their reconstructing performances for CS-based ECG signal processing lacks, especially in real-time applications. This study aimed to investigate this issue and provide useful information. Four typical recovery algorithms, i.e., compressed sampling matching pursuit (CoSaMP), orthogonal matching pursuit (OMP), expectation-maximum-based block sparse Bayesian learning (BSBLEM) and bound-optimization-based block sparse Bayesian learning (BSBL BO) were compared. Two performance indices, i.e., the percentage of root-mean-square difference (PRD) and the reconstructing time (RT), were tested to observe their changes with the change of compression ratio (CR). The results showed that BSBL_BO and BSBL_EM methods had better performances than OMP and CoSaMP methods. More specifically, BSBL_BO reported the best PRD results while BSBL_EM achieved the best RT index.