Electrocardiogram Reconstruction Based on Compressed Sensing

被引:11
|
作者
Zhang, Zhimin [1 ,2 ]
Liu, Xinwen [2 ]
Wei, Shoushui [1 ]
Gan, Hongping [3 ]
Liu, Feifei [1 ]
Li, Yuwen [4 ]
Liu, Chengyu [5 ]
Liu, Feng [2 ]
机构
[1] Shandong Univ, Sch Control Sci & Engn, Jinan 250100, Shandong, Peoples R China
[2] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia
[3] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Shaanxi, Peoples R China
[4] Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
[5] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Compressed sensing (CS); compression; electrocardiogram (ECG); reconstruction; subsampling; ECG; ALGORITHMS; RECOVERY; SYSTEM;
D O I
10.1109/ACCESS.2019.2905000
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Compressed Sensing (CS) attempts to acquire and reconstruct a sparse signal from a sampling much below the Nyquist rate. In this paper, we proposed novel CS algorithms for reconstructing under-sampled and compressed electrocardiogram (ECG) signal. In the proposed CS-ECG scheme, the ECG signal was first sub-sampled randomly and mapped onto a two-dimensional (2D) space by using Cut and Align (CAB), for the purpose of promoting sparsity. A nonlinear optimization model was then used to reconstruct the 2D signal. In the compression scheme, the ECG signal was mapped into the frequency domain, and the compression was achieved by a series of multiplying and accumulating between the original ECG and a Gaussian random matrix. For the reconstruction, two matching pursuits (MP) methods and two blocks sparse Bayesian learning (BSBL) methods were implemented and evaluated by the percentage root-mean-square difference (PRD). Based on the test with real ECG data, it was found that the proposed CS scheme was capable of faithfully reconstructing ECG signals with only 30% acquisition.
引用
收藏
页码:37228 / 37237
页数:10
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