Electrocardiogram Reconstruction Based on Compressed Sensing

• Main Authors: Zhimin Zhang, Xinwen Liu, Shoushui Wei, Hongping Gan, Feifei Liu, Yuwen Li, Chengyu Liu, Feng Liu
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Compressed sensing (CS); compression; electrocardiogram (ECG); reconstruction; subsampling
• Online Access: https://ieeexplore.ieee.org/document/8667447/

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.