Electrocardiogram Reconstruction Based on Compressed Sensing

IEEE Access

Chengyu Liu¹  Shoushui Wei²  Zhimin Zhang²  Yuwen Li²  Xinwen Liu³  Feng Liu⁴  Hongping Gan⁵  Feifei Liu⁶ 

[1] Department of Automation, Xiamen University, Xiamen, China;School of Control Science and Engineering, Shandong University, Jinan, China;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia;School of Instrument Science and Engineering, Southeast University, Nanjing, China;State Key Laboratory of Integrated Services Networks, Xidian University, Xi&x2019;an, China;

关键词: Compressed sensing (CS);    compression;    electrocardiogram (ECG);    reconstruction;    subsampling;   

DOI  :  10.1109/ACCESS.2019.2905000

来源: DOAJ

【 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.

【 授权许可】

Unknown