Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

TL;DR

This paper enhances compressed sensing for wireless ECG systems by leveraging wavelet structure and prior information, significantly improving compression and reconstruction quality over existing CS methods.

Contribution

It introduces a novel approach that exploits wavelet dependencies and common support across ECG segments to boost CS-based compression and reconstruction performance.

Findings

Significant improvement in compression rate and reconstruction quality.

Experimental validation on MIT-BIH database confirms effectiveness.

Proposed method outperforms current CS-based algorithms.

Abstract

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.