Compressive sensing based the multi-channel ECG reconstruction in wireless body sensor networks

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. (C) 2020 Elsevier Ltd. All rights reserved.