• DocumentCode
    2372889
  • Title

    A semi-automated method for epileptiform transient detection in the EEG of the fetal sheep using time-frequency analysis

  • Author

    Walbran, Anita C. ; Unsworth, Charles P. ; Gunn, Alistair J. ; Bennet, Laura

  • Author_Institution
    Dept. of Eng. Sci., Univ. of Auckland, Auckland, New Zealand
  • fYear
    2009
  • fDate
    3-6 Sept. 2009
  • Firstpage
    17
  • Lastpage
    20
  • Abstract
    Perinatal hypoxia remains a significant cause of brain damage. Currently there are no biomarkers to detect the at risk brain. Recent research, however, suggests that the appearance of epileptiform transients in the first 6-8 hours after hypoxia (the latent phase of injury) are predictive of neural outcome. To quantify this further a key need is to automate EEG signal analysis to aid clinical staff with the vast amounts of complex data to review. In this study, we present a semi-automated method for spike detection in the fetal sheep EEG. The method utilizes the short time Fourier transform and peak separation to extract spikes. The performance of the method was found to be high in sensitivity and selectivity over 3 distinct time points.
  • Keywords
    Fourier transforms; bioelectric phenomena; electroencephalography; medical signal processing; neurophysiology; time-frequency analysis; automate EEG signal analysis; brain damage; epileptiform transient detection; fetal sheep EEG; peak separation; perinatal hypoxia; semiautomated method; short time Fourier transform; spike detection; time-frequency analysis; Algorithms; Animals; Artificial Intelligence; Diagnosis, Computer-Assisted; Electroencephalography; Epilepsy; Fetal Diseases; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Sheep;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
  • Conference_Location
    Minneapolis, MN
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-3296-7
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/IEMBS.2009.5332431
  • Filename
    5332431