Information‐enhanced sparse binary matrix in compressed sensing for ECG | Zendy

Luo Kan | Zendy; Wang Zhigang | Zendy; Li Jianqing | Zendy; Yanakieva R. | Zendy; Cuschieri A. | Zendy

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Information‐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 , binary number , matrix (chemical analysis) , computer science , sparse matrix , algorithm , pattern recognition (psychology) , artificial intelligence , materials science , mathematics , physics , arithmetic , quantum mechanics , composite material , gaussian

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.

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