Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction

Abstract

In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is further to be improved. In this paper, we provide an efficient compressive sensing framework which strives to improve the reconstruction process, by adjusting the sensing matrix during the compression phase using the rain optimization algorithm (ROA). With the optimal sensing matrix, the compressed signal is reconstructed using Step Size optimized Sparsity Adaptive Matching Pursuit algorithm (SAMP). The results of this work demonstrate that the optimised CS framework achieves a higher compression rate and probability of reconstruction than the standard CS framework.

An Improved Measurement Matrix Generator for Compressed Sensing of ECG Signals

“…Wireless Body Area Networks (WBANs) have been widely used in bioelectrical signal monitoring because of the advantages of portability, comfort, and security. Nevertheless, the implementation of WBANs still faces many challenges such as high power consumption and large area [6][7][8]. Data transmission occupies most of the total power consumption in WBANs, and the power consumption is positively correlated with the amount of data transmitted.…”