通过压缩感知实现生理信号的节能远程监测：快速算法和功耗评估
Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation
摘要：
生理信号的无线远程监测是eHealth中的重要课题。为了减少片上能耗和延长传感器寿命，记录的信号通常在传输前进行压缩。本文采用压缩感知（CS）作为低功耗压缩框架，提出了一种快速块稀疏贝叶斯学习（BSBL）算法来重构原始信号。对真实胎儿心电信号和癫痫脑电信号的实验结果表明，与现有的CS算法相比，该算法在重建速度和数据重建精度之间取得了较好的平衡。此外，我们在现场可编程门阵列（FPGA）中实现了基于CS的压缩过程和基于小波变换的低功耗压缩过程，表明基于CS的压缩可以大大节省能量和其他片上计算资源。
英文摘要：
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.
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