Title :
Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors
Author :
Dixon, A.M.R. ; Allstot, E.G. ; Gangopadhyay, D. ; Allstot, D.J.
Author_Institution :
Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA
fDate :
4/1/2012 12:00:00 AM
Abstract :
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.
Keywords :
biosensors; body area networks; body sensor networks; compressed sensing; data reduction; electrocardiography; electromyography; medical signal detection; medical signal processing; ECG wireless biosensors; EMG wireless biosensors; biosignal acquisition systems; compressed sensing system; data reduction; electrocardiogram biosignals; electromyogram biosignals; encoder bit-precision requirement; signal processing paradigm; signal recovery algorithms; signal-to-quantization noise ratio; sparse signals; subNyquist processing; thresholding techniques; ultralow-power performance; Compressed sensing; Electrocardiography; Electromyography; Sparse matrices; Testing; Vectors; Biosignal sensors; body-area networks (BAN); compressed sensing (CS); compressive sampling; electrocardiogram (ECG); electromyogram (EMG); sparsity;
Journal_Title :
Biomedical Circuits and Systems, IEEE Transactions on
DOI :
10.1109/TBCAS.2012.2193668
