Energy-Efficient ECG Compression on Wireless Biosensors via Minimal Coherence Sensing and Weighted <formula formulatype="inline"><tex Notation="TeX">$\ell_1$</tex></formula> Minimization Reconstruction

Abstract

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

Exploiting interchannel correlations in multichannel compressed sensing ECG systems

“…But noting the advantage of joint multiscale processing in the CS framework (in terms of significant gain in diagnostic quality of reconstructed MECG signals), a small computation may be tolerated. From the power efficiency point of view, a random sparse binary sensing matrix is employed during the entire encoding process with entries of 0's and 1's [7]. This cuts down the additional computational cost during signal sensing operation as their will be only addition and not multiplication operations at the encoder.…”

“…In a similar framework, Zhang et al [5] proposed a system for fetal-ECG tele-monitoring employing a Sparse Bayesian Learning (SBL) based approach. Recently, prior knowledge based reconstruction algorithms have also been proposed and improved results are reported [6], [7].…”

High diagnostic quality ECG compression and CS signal reconstruction in body sensor networks

“…The development of small and portable ECG systems with low energy consumption has been a topic of interest for the last years [1][2][3]. Several achievements have been obtained, including a wireless Body Sensor Network (BSN) capable of sampling and transmitting a one-lead ECG [1].…”

Compressed sensing framework of data reduction at multiscale level for eigenspace multichannel ECG signals

“…Recent advancements in the field of ECG compression are the techniques based on compressive sensing (CS) [7], [8] approach which has been presented recently as a striking alternative of wavelet transform based compression techniques for real time compression applications [9]- [14]. In ECG signals, a fair amount of sparseness exist in time-domain [9] which may get enhanced many folds in some transform domains (e.g.wavelet domain).…”

“…In ECG signals, a fair amount of sparseness exist in time-domain [9] which may get enhanced many folds in some transform domains (e.g.wavelet domain). This sparseness is exploited for data reduction using CS based techniques [10]- [14]. In recent years, CS has emerged as a powerful tool in the signal processing arena due to it ability to confine high dimensional sparse signals to few random projections in a low dimensional subspace.…”