title: Sparse electrocardiogram signals recovery based on solving a row echelon-like form of system.
abstract: 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.
authors.affiliation: Department of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China.
authors.forename: Pingmei
authors.initials: P
authors.lastname: Cai
authors.affiliation: Department of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China.
authors.forename: Guinan
authors.initials: G
authors.lastname: Wang
authors.affiliation: Department of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China.
authors.forename: Shiwei
authors.initials: S
authors.lastname: Yu
authors.affiliation: Department of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China. zhanghongjuan@shu.edu.cn.
authors.forename: Hongjuan
authors.initials: H
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-Machi, Aizu-Wakamatsu City, Fukushima 965-8580, Japan.
authors.forename: Shuxue
authors.initials: S
authors.lastname: Ding
authors.affiliation: Business School, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
authors.forename: Zikai
authors.initials: Z
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods*; D000069550:Machine Learning; D012815:Signal Processing, Computer-Assisted
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