title: Compressed sensing system considerations for ECG and EMG wireless biosensors.
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.
authors.affiliation: Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
authors.forename: Anna M R
authors.initials: AM
authors.lastname: Dixon
authors.forename: Emily G
authors.initials: EG
authors.lastname: Allstot
authors.forename: Daibashish
authors.initials: D
authors.lastname: Gangopadhyay
authors.forename: David J
authors.initials: DJ
authors.lastname: Allstot
mesh_terms: D015374:Biosensing Techniques / Q000295:instrumentation*; D003195:Computer Communication Networks / Q000295:instrumentation; D044962:Data Compression / Q000379:methods*; D004562:Electrocardiography / Q000295:instrumentation*; D004576:Electromyography / Q000295:instrumentation*; D004867:Equipment Design; D006801:Humans; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D013997:Time Factors; D058067:Wavelet Analysis; D059015:Wireless Technology / Q000295:instrumentation*