JSM-2 based joint ECG compressed sensing with partially known support establishment

Abstract

Compressed sensing (CS) is a technique that enables sparse signal reconstruction from much fewer samples. In this paper, we propose ECG compressed sensing methods based on distributed compressed sensing to exploit the joint sparsity for both single- and multi-lead ECG signals. We apply JSM-2 (joint sparse model type 2) for jointly sparse ECG signals and formulate how to establish a partially known support based on this type of sparse model. Through careful analysis of joint partially known support, two-step ECG signal reconstruction schemes for single-lead and multi-lead ECG signals are developed. Simulation results show that the proposed schemes based on partially known support establishment outperforms existing schemes with enhanced performance measured by percentage root mean square difference (PRD).