Open AccessInformation‐enhanced sparse binary matrix in compressed sensing for ECG
Author(s) -
Luo Kan,
Wang Zhigang,
Li Jianqing,
Yanakieva R.,
Cuschieri A.
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.1749
Subject(s) - compressed sensing , entropy (arrow of time) , binary number , computer science , signal (programming language) , pattern recognition (psychology) , matrix (chemical analysis) , artificial intelligence , prior information , compression (physics) , sparse matrix , information theory , algorithm , mathematics , materials science , statistics , physics , arithmetic , quantum mechanics , composite material , gaussian , programming language
An information‐enhanced sparse binary matrix (IESBM) is proposed to improve the quality of the recovered ECG signal from compressed sensing. With the detection of the area of interest and the enhanced measurement model, the IESBM increases the information entropy of the compressed signal and preserves more information during compression; thus, it guarantees a high‐quality recovery. The experimental results indicate that the proposed matrix is suitable for compressed sensing of the ECG signal with small distortions in both overall and the concerned diagnostic segments.