Open AccessSparse electrocardiogram signals recovery based on solving a row echelon‐like form of system
Author(s) -
Cai Pingmei,
Wang Guinan,
Yu Shiwei,
Zhang Hongjuan,
Ding Shuxue,
Wu Zikai
Publication year - 2016
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2015.0002
Subject(s) - linear subspace , computer science , noise (video) , sparse matrix , matrix (chemical analysis) , transformation (genetics) , algorithm , signal (programming language) , point (geometry) , signal processing , time domain , mixing (physics) , pattern recognition (psychology) , artificial intelligence , mathematics , computer vision , telecommunications , biochemistry , chemistry , physics , geometry , materials science , radar , composite material , quantum mechanics , gene , image (mathematics) , gaussian , programming language
The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine. Due to its periodic characteristic, ECG signal can be roughly regarded as sparse biomedical signals. This study proposes a two‐stage recovery algorithm for sparse biomedical signals in time domain. In the first stage, the concentration subspaces are found in advance. Then by exploiting these subspaces, the mixing matrix is estimated accurately. In the second stage, based on the number of active sources at each time point, the time points are divided into different layers. Next, by constructing some transformation matrices, these time points form a row echelon‐like system. After that, the sources at each layer can be solved out explicitly by corresponding matrix operations. It is noting that all these operations are conducted under a weak sparse condition that the number of active sources is less than the number of observations. Experimental results show that the proposed method has a better performance for sparse ECG signal recovery problem.