Compressed sensing based ECG signal compression for telemedicine

Abstract

An Electrocardiogram (ECG) signal compression technique is proposed using compressed sensing/sampling technique based on Block sparse Bayesian learning (BSBL) algorithm. Advantage of proposed method over the conventional stat-of-art techniques is energy efficient, highly compressive and minimum reconstruction error. Here, BSBL technique has utilized especially for compression of ECG signal to enhance the performance data handling/communication system of telemedicine. Simulated results have achieved 75% of compression with very good quality of reconstruction. Therefore, it's clearly illustrate the proposed technique is gives grate efficiency as per aim of work and comparatively efficient than stat-of-art techniques.