JSM-2 based ECG compression with statistical support prediction
This paper addresses the problem of developing an efficient compression scheme with high quality and low computational complexity for ECG signal compression. Taking into account the joint sparsity existing in ECG data and the temporal dependencies in ECG signal sequence, a novel scheme for JSM-2 based ECG compression is developed to exploit these characteristics. We first predict support information in sparse domain from the previous ECG data for the current recovery process. Then a modified Simultaneous Orthogonal Matching Pursuit Algorithm (SOMP) algorithm is proposed to incorporate the idea of support information establishment for JSM-2 based ECG compression. Simulation results show that the proposed JSM-2 based ECG compression scheme with statistical support prediction outperforms existing schemes with enhanced performance and low computational complexity.