标题：Comparison of Four Recovery Algorithms used in Compressed Sensing for ECG Signal Processing
作者：Zhang, Zhimin; Wei, Shoushui; Wei, Dingwen; Li, Liping; Liu, Feng; Liu, Chengyu
作者机构：[Zhang, Zhimin; Wei, Shoushui; Liu, Chengyu] Shandong Univ, Sch Control Sci & Engn, 17923 Jingshi Rd, Jinan 250061, Peoples R China.; [Wei, Dingwen] 更多
会议名称：43rd Computing in Cardiology Conference (CinC)
会议日期：SEP 11-14, 2016
来源：2016 COMPUTING IN CARDIOLOGY CONFERENCE (CINC), VOL 43
摘要：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 (BSBL_EM) 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.