Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
IEEE Trans Biomed Circuits Syst. 2012 Apr;6(2):156-66. doi: 10.1109/TBCAS.2012.2193668.
Compressed sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist processing of sparse signals such as electrocardiogram (ECG) and electromyogram (EMG) biosignals. Consequently, it can be applied to biosignal acquisition systems to reduce the data rate to realize ultra-low-power performance. CS is compared to conventional and adaptive sampling techniques and several system-level design considerations are presented for CS acquisition systems including sparsity and compression limits, thresholding techniques, encoder bit-precision requirements, and signal recovery algorithms. Simulation studies show that compression factors greater than 16X are achievable for ECG and EMG signals with signal-to-quantization noise ratios greater than 60 dB.
压缩感知（CS）是一种新兴的信号处理范例，可实现稀疏信号（如心电图（ECG）和肌电图（EMG）生物信号）的亚奈奎斯特处理。因此，它可以应用于生物信号采集系统，以降低数据速率，实现超低功耗性能。CS 与传统和自适应采样技术进行了比较，并针对 CS 采集系统提出了几个系统级设计注意事项，包括稀疏度和压缩限制、阈值技术、编码器位精度要求以及信号恢复算法。仿真研究表明，对于信噪比大于 60dB 的 ECG 和 EMG 信号，可实现大于 16X 的压缩因子。
