Sparse electrocardiogram signals recovery based on solving a row echelon-like form of system
Sparse electrocardiogram signals recovery based on solving a row echelon-like form of system
复制标题
基于求解行梯形系统的稀疏心电信号恢复
DOI：
10.1049/iet-syb.2015.0002
复制
发表时间：
2016
影响因子：
2.3
通讯作者：
Wu Zikai
中科院分区：
文献类型：
--
作者：
Cai Pingmei;Wang Guinan;Yu Shiwei;Zhang Hongjuan;Ding Shuxue;Wu Zikai
噪声环境下的生物学和医学研究是生物数据分析的一个发展方向。在这些研究中，在噪声环境中的心电图（ECG）信号的分析是个性化医疗的一个具有挑战性的方向。由于心电信号具有周期性的特点，可以将其粗略地看作是稀疏的生物医学信号。本研究提出一种两阶段的时域稀疏生物医学信号恢复算法。在第一阶段，集中子空间被提前发现。然后利用这些子空间，精确估计混合矩阵。在第二阶段中，基于每个时间点的活动源的数量，将时间点划分为不同的层。接下来，通过构造一些变换矩阵，这些时间点形成一个行梯队状系统。然后通过相应的矩阵运算，显式求解出每一层的源。值得注意的是，所有这些操作都是在弱稀疏条件下进行的，即活动源的数量小于观测的数量。实验结果表明，该方法对稀疏心电信号恢复问题具有较好的性能。
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.