  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.
