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Compressive sampling of ECG bio-signals: Quantization noise and sparsity considerations

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5 Author(s)
Emily G. Allstot ; Dept. of Electrical Engineering, Univ. of Washington, Seattle, WA, USA ; Andrew Y. Chen ; Anna M. R. Dixon ; Daibashish Gangopadhyay
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Compressed sensing (CS) is an emerging signal processing paradigm that enables the sub-Nyquist processing of sparse signals; i.e., signals with significant redundancy. Electrocardiogram (ECG) signals show significant time-domain sparsity that can be exploited using CS techniques to reduce energy consumption in an adaptive data acquisition scheme. A measurement matrix of random values is central to CS computation. Signal-to-quantization noise ratio (SQNR) results with ECG signals show that 5- and 6-bit Gaussian random coefficients are sufficient for compression factors up to 6X and from 8X-16X, respectively, whereas 6-bit uniform random coefficients are needed for 2X-16X compression ratios.

Note: Author names were incorrectly displayed as Emily, G. Allstot Andrew, Y. Chen Anna, M. R. Dixon Gangopadhyay, Daibashish David, J. Allstot shoud read Allstot, Emily; Chen, Andrew; Dixon, Anna; Gangopadhyay, Daibashish; Allstot, David  

Published in:

2010 Biomedical Circuits and Systems Conference (BioCAS)

Date of Conference:

3-5 Nov. 2010