• DocumentCode
    2955593
  • Title

    Adaptive asynchronous analog to digital conversion for compressed biomedical sensing

  • Author

    Agarwal, R. ; Trakimas, M. ; Sonkusale, S.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Tufts Univ., Medford, MA, USA
  • fYear
    2009
  • fDate
    26-28 Nov. 2009
  • Firstpage
    69
  • Lastpage
    72
  • Abstract
    Compressed sensing enables direct analog to digital information conversion of signals at rates much lower than the Nyquist rate. This eliminates the need for computationally intensive, high speed data acquisition and digital signal processing for compression. We illustrate the use of an asynchronous analog to digital converter (ADC) as a low power, low complexity compressed sensing digitizer of biomedical signals at source. A variable input-slope dependent adaptive technique is proposed for increased compression, reduced slope overload distortion error for fast moving signals, and reduced power consumption. ECG signal compression in a simulated environment, shows large compression for a given mean squared error compared to synchronous Nyquist rate A/D converters and a regular asynchronous A/D converter.
  • Keywords
    analogue-digital conversion; biomedical electronics; data compression; electrocardiography; low-power electronics; medical signal processing; ECG signal compression; adaptive asynchronous analog-digital conversion; asynchronous A-D converter comparison; biomedical signal digitiser; compressed biomedical sensing; fast moving signals; low power low complexity compressed sensing digitiser; slope overload distortion error; synchronous Nyquist rate A-D converter comparison; variable input slope dependent adaptive technique; Analog-digital conversion; Biomedical imaging; Compressed sensing; Data acquisition; Data compression; Distortion; Energy consumption; Quantization; Signal resolution; Signal sampling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Circuits and Systems Conference, 2009. BioCAS 2009. IEEE
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-4917-0
  • Electronic_ISBN
    978-1-4244-4918-7
  • Type

    conf

  • DOI
    10.1109/BIOCAS.2009.5372083
  • Filename
    5372083