Signal agnostic compressive sensing for Body Area Networks: comparison of signal reconstructions.
Compressive sensing is a lossy compression technique that is potentially very suitable for use in power constrained sensor nodes and Body Area Networks as the compression process has a low computational complexity. This paper investigates the reconstruction performance of compressive sensing when applied to EEG, ECG, EOG and EMG signals; establishing the performance of a signal agnostic compressive sensing strategy that could be used in a Body Area Network monitoring all of these. The results demonstrate that the EEG, ECG and EOG can all be reconstructed satisfactorily, although large inter- and intra- subject variations are present. EMG signals are not well reconstructed. Compressive sensing may therefore also find use as a novel method for the identification of EMG artefacts in other electro-physiological signals.
Department of Electrical and Electronic Engineering, Imperial College London, UK. acasson@imperial.ac.uk
Alexander J
AJ
Casson
Esther
E
Rodriguez-Villegas
D000465:Algorithms; D016477:Artifacts; D003195:Computer Communication Networks; D044962:Data Compression / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004568:Electrodiagnosis / Q000379:methods*; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted