Compressive sampling of EMG bio-signals

Sub-Nyquist analog pre-processing of sparse signals is achieved using the emerging compressed sensing (CS) signal processing paradigm. Electrocardiogram (ECG) signals have been shown previously to have significant time-domain sparsity. It is shown herein that electromyogram (EMG) signals exhibit both time and frequency-domain sparsity. Hence, CS techniques are advantageous in either domain in reducing the energy consumption in an adaptive data acquisition front-end that is part of a body area network (BAN). A measurement matrix of random values is central to CS computation. Signal-to quantization-noise ratio (SQNR) results with EMG signals show that 6-bit (including sign) Gaussian random coefficients are sufficient for compression factors up to 18X. It is also shown that 6-bit uniform random coefficients are preferred for some EMG bio-signals.