<div>The aim of this study is to propose an information-theoretic</div><div>framework that can be used for joint recovery of sparse</div><div>source biosignals. The proposed method supports medical cyber-physical systems (CPS) that enhance the detection, tracking, and monitoring of vital signs via wearable biosensors. Specifically, we address the problem of sparse signal recovery and acquisition in wearable biosensor networks, where we develop an adaptive design methodology based on compressed sensing (CS) and</div><div>independent component analysis (ICA) to reduce and eliminate artifacts and interference in sparse biosignals. Our analysis and examples offer a low-complexity algorithm design for patient monitoring systems, where sparse source biosignals can be recovered at low hardware costs and power consumption. Also, we show that, under noisy measurement conditions, the joint CS-ICA recovery algorithms can outperform standard CS methods, where a sparse biosignal is retrieved in a few measurement. By implementing the joint sparse recovery algorithms, the error in reconstructing sparse biosignals is reduced, and a digital-to-analog converter operates at low-speed and low-resolution.</div>
Cyber-Physical Systems: An Information-Theoretic Framework for Joint CS-ICA Recovery of Sparse Source Biosignals.
