Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors

被引：243

作者：

Dixon, Anna M. R. ^{[1
]}

Allstot, Emily G. ^{[1
]}

Gangopadhyay, Daibashish

Allstot, David J. ^{[1
]}

机构：

[1] Univ Washington, Dept Elect Engn, Seattle, WA 98195 USA

来源：

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS | 2012年 / 6卷 / 02期

基金：

美国国家科学基金会;

关键词：

Biosignal sensors; body-area networks (BAN); compressed sensing (CS); compressive sampling; electrocardiogram (ECG); electromyogram (EMG); sparsity; SIGNAL RECOVERY; HARDWARE;

D O I：

10.1109/TBCAS.2012.2193668

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：156 / 166

页数：11

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