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

Healthcare Technology Letters

article

Anurag Singh¹  Samarendra Dandapat¹ 

[1] Electro Medical and Speech Technology Laboratory, Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati

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;   

DOI  :  10.1049/htl.2016.0049

学科分类：肠胃与肝脏病学

来源: Wiley

【 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.

【 授权许可】

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