• DocumentCode
    517787
  • Title

    Low power compression of EEG signals using JPEG2000

  • Author

    Higgins, Garry ; McGinley, B. ; Glavin, Martin ; Jones, Edward

  • Author_Institution
    Bioelectronics Res. Cluster, Nat. Univ. of Ireland Galway, Galway, Ireland
  • fYear
    2010
  • fDate
    22-25 March 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    This paper outlines a scheme for compressing EEG signals based on the JPEG2000 image compression algorithm. Such a scheme could be used to compress signals in an ambulatory system, where low-power operation is important to conserve battery life; therefore, a high compression ratio is desirable to reduce the amount of data that needs to be transmitted. The JPEG2000 specification makes use of the wavelet transform, which can be efficiently implemented in embedded systems. The standard was broken down to its core components and adapted for use on EEG signals with additional compression steps added. Variations on the components were tested to maximize compression ratio (CR) while maintaining a low percentage root-mean-squared difference (PRD) and minimize power requirements. Initial tests indicate that the algorithm performs well in relation to other EEG compression methods proposed in the literature.
  • Keywords
    data compression; electroencephalography; image coding; mean square error methods; medical image processing; wavelet transforms; EEG signal compression; JPEG2000; image compression; low power compression; multichannel electroencephalogram; percentage root mean squared difference; wavelet transform; Chromium; Electric variables measurement; Electroencephalography; Energy consumption; Image coding; Medical services; Patient monitoring; Remote monitoring; Testing; Transform coding; EEG compression; JPEG2000; Wavelets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on-NO PERMISSIONS
  • Conference_Location
    Munich
  • Print_ISBN
    978-963-9799-89-9
  • Electronic_ISBN
    978-963-9799-89-9
  • Type

    conf

  • DOI
    10.4108/ICST.PERVASIVEHEALTH2010.8861
  • Filename
    5482256