Multichannel ECG Compression using Block-Sparsity-based Joint Compressive Sensing

Abstract

Wireless body area networks (WBANs) are increasingly used for remote healthcare surveillance in recent times, where electrocardiogram (ECG) signals are continuously acquired and transmitted to a base station or remote hospital for their storage and subsequent analysis. Multichannel ECG (MECG) is preferred over single-channel ECG as it provides more information from diagnostic point of view. One of the biggest challenges is to minimize the energy required for the WBAN network for continuous transmission of MECG data, which in turn demands for efficient data compression. Compressive sensing is an efficient signal processing tool for simultaneous compression and reconstruction of MECG data without visibly no or minimum loss of diagnostic information. In this paper, we propose an energy-efficient novel block-sparsity-based MECG compression scheme, which exploits both spatiotemporal correlation and multi-scale information of MECG data in the wavelet domain, effectively. Experimental results show that the proposed method outperforms other recently developed methods for MECG compression both qualitatively and quantitatively.