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Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors

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4 Author(s)
Anna M. R. Dixon ; Dept. of Electrical Engineering, Univ. of Washington, Seattle ; Emily G. Allstot ; Daibashish Gangopadhyay ; David J. Allstot

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.

Published in:

IEEE Transactions on Biomedical Circuits and Systems  (Volume:6 ,  Issue: 2 )