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Wireless telemonitoring of physiological signals is an important topic ineHealth. In order to reduce on-chip energy consumption and extend sensor life,recorded signals are usually compressed before transmission. In this paper, weadopt compressed sensing (CS) as a low-power compression framework, and proposea fast block sparse Bayesian learning (BSBL) algorithm to reconstruct originalsignals. Experiments on real-world fetal ECG signals and epilepsy EEG signalsshowed that the proposed algorithm has good balance between speed and datareconstruction fidelity when compared to state-of-the-art CS algorithms.Further, we implemented the CS-based compression procedure and a low-powercompression procedure based on a wavelet transform in Filed Programmable GateArray (FPGA), showing that the CS-based compression can largely save energy andother on-chip computing resources.

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