<div>The aim of this study is to propose an information-theoretic framework
for compressed sensing (CS)-independent</div>


<div>component analysis (ICA) algorithms that can be used for the joint
recovery of sparse biosignals. The proposed framework supports real-time
patient monitoring systems that enhance the detection, tracking, and
monitoring of vital signs remotely via wearable biosensors.
Specifically, we address the problem of sparse signal recovery and
acquisition in wearable biosensor networks, where we</div>


<div>present a new analysis of CS-ICA algorithms from an information theory
perspective to compute the sampling rate required to recover sparse
biosignals corrupted by motion artifacts and interference, which to the
best of our knowledge, has not been studied before. Our analysis and
examples indicate that the proposed approach helps to develop low-cost,
low-power edge computing devices while</div>


<div>improving data quality and accuracy for a given measurement. We also
show that, under noisy measurement conditions, the CS-ICA algorithm can
outperform the standard CS method, where a biosignal can be retrieved in
only a few measurements. By implementing</div>


<div>the sensing framework, the error in reconstructing biosignals is
reduced, and a digital-to-analog converter operates at low-speed and
low-resolution.</div>