Compressive sensing based the multi-channel ECG reconstruction in wireless body sensor networks. (August 2020)

• Record Type: Journal Article
• Authors: Afshar Jahanshahi, Javad; Danyali, Habibollah; Helfroush, Mohammad Sadegh
• Is Part Of: Biomedical signal processing and control. Volume 61(2020)
• Publication Date: 2020-08
• Subjects: Compressed sensing (CS) -- Wireless body sensor networks -- Multi-channel ECG signals; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102047
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A CS-based method with low-rank constraint is proposed for effective data acquisition and signal reconstruction in the energy-constrained WBSN. An optimization formula consisting of two constraints is defined; the sparsity constraint and the low-rank constraint. A robust and efficient ADMM-based method is developed to efficiently reconstruct the multichannel ECG signals. Numerical experiments verify that the proposed algorithm achieves superior performance as compared to the latest CS-based recovery methods. Abstract: Compressed Sensing (CS) has been considered a very effective means of reducing energy consumption at the energy-constrained wireless body sensor networks for monitoring the multi-channel Electrocardiogram (MECG) signals. In this paper, we have used the Kronecker sparsifying bases to exploit the spatio-temporal correlations of the MECG signals for improving the compression of the signals transmitted by the sensors. Furthermore, a compressed sensing-based method with low-rank constraint is proposed for effective data acquisition and signal reconstruction in the energy-constrained wireless body sensor networks. More specifically, in the proposed algorithm, an optimization formula consisting of two constraints is defined. The sparsity constraint is presented through the minimization of the l 1 norm and the low-rank constraint is specified through the minimization of the nuclear norm. Afterward, a robust and efficient alternating direction method of multipliers (ADMM) based method is developed for the reconstruction of the MECG signals that solves the resulting optimization problem more effectively. Numerical experiments verify that the proposed algorithm achieves greater reconstruction accuracy with the smaller number of required transmissions, lower computational complexity, and smaller reconstruction errors, as compared to the latest CS-based recovery methods.