Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation
Abstract
Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signals. Experiments on real-world fetal ECG signals and epilepsy EEG signals showed that the proposed algorithm has good balance between speed and data reconstruction fidelity when compared to state-of-the-art CS algorithms. Further, we implemented the CS-based compression procedure and a low-power compression procedure based on a wavelet transform in Filed Programmable Gate Array (FPGA), showing that the CS-based compression can largely save energy and other on-chip computing resources.
Cite
@article{arxiv.1309.7843,
title = {Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation},
author = {Benyuan Liu and Zhilin Zhang and Gary Xu and Hongqi Fan and Qiang Fu},
journal= {arXiv preprint arXiv:1309.7843},
year = {2014}
}