Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals

[摘要] In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

[学科分类] 肠胃与肝脏病学

[关键词] electrocardiography;medical signal processing;data compression;data reduction;body area networks;telemedicine;patient monitoring;correlation methods;Bayes methods;learning (artificial intelligence);spatiotemporal phenomena;signal reconstruction;discrete wavelet transforms;block sparsity-based joint compressed sensing recovery;multichannel ECG signals;MECG signals;data compression;energy-efficient data reduction;resource-constrained wireless body area network;WBAN;telemonitoring;spatial correlations;temporal correlations;sparse Bayesian learning;spatiotemporal sparse model;signal reconstruction;discrete wavelets transform;Physikalisch-Technische Bundesanstalt MECG diagnostic database