Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation

TL;DR

This paper introduces a fast compressed sensing algorithm for wireless physiological signal telemonitoring, demonstrating significant energy savings and efficient signal reconstruction on FPGA hardware.

Contribution

It proposes a novel fast BSBL algorithm for signal reconstruction and evaluates its energy efficiency in FPGA implementations, advancing low-power telemonitoring technology.

Findings

The proposed BSBL algorithm balances speed and fidelity effectively.

CS-based compression significantly reduces energy consumption.

FPGA implementation confirms energy savings and resource efficiency.

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.