ECG signal compression and denoising via optimum sparsity order selection in compressed sensing framework. (March 2018)

• Record Type: Journal Article
• Authors: Yousefi Rezaii, Tohid; Beheshti, Soosan; Shamsi, Mahdi; Eftekharifar, Siavash
• Is Part Of: Biomedical signal processing and control. Volume 41(2018)
• Page Start: 161
• Page End: 171
• Publication Date: 2018-03
• Subjects: Compressed sensing -- ECG compression -- Sparse representation -- Model order selection -- Data denoising; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.11.015
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Abstract: Advanced signal processing is widely used in healthcare systems and equipment. Compressing ECG signals is beneficial in long-term monitoring of patients' behavior. Compressed Sensing (CS) based ECG compression has shown superiority over the existing ECG compression approaches. In current CS ECG compression methods, sparsity order (number of basis vectors involved in the compression) is determined either empirically or by thresholding approaches. Here, we propose a new method denoted by Optimum Sparsity Order Selection (OSOS) that calculates the sparsity order by minimizing reconstruction error. In addition, we have shown that basis matrix based on raised Cosine kernel has more efficiency in compression over the Gaussian basis matrices. The fundamentals of OSOS algorithm is such that the method is robust to observation noise. Simulation results confirm efficiency of our method in terms of compression ratio.