Energy efficient telemonitoring of physiological signals via compressed sensing: A fast algorithm and power consumption evaluation

• We developed a fast compressed sensing (CS) algorithm based on the block sparse Bayesian learning (BSBL) framework.
• We systematically evaluated the distortions of CS-based data compression on two real-life applications, e.g., fetal ECG (FECG) telemonitoring and EEG telemonitoring for the epileptic patients.
• Using an FPGA platform, we showed that the CS-based compression method, compared to a low-power wavelet-based compression method, can largely reduce power consumption and save other computing resources.

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 field programmable gate array (FPGA), showing that the CS-based compression can largely save energy and other on-chip computing resources.