• DocumentCode
    179713
  • Title

    Grading brain injury in neonatal EEG using SVM and supervector kernel

  • Author

    Ahmed, Rizwan ; Temko, Andriy ; Marnane, William ; Boylan, Geraldine ; Lightbody, G.

  • Author_Institution
    Neonatal Brain Res. Group, Irish Center for Fetal & Neonatal Translational Res., Ireland
  • fYear
    2014
  • fDate
    4-9 May 2014
  • Firstpage
    5894
  • Lastpage
    5898
  • Abstract
    Brain injury at the time of birth could lead to severe neurological dysfunction at an older age. Grading the brain injury in the early hours after birth could help doctors determine a prompt and reliable treatment. This work presents an automated neonatal EEG grading system based on a cross-disciplinary method of using Support Vector Machine and supervectors, initially developed for speaker identification. The EEG is classified into one of the four grades of neonatal brain injury. The preliminary results show promising performance and are an improvement on the previously published results.
  • Keywords
    electroencephalography; neurophysiology; support vector machines; automated neonatal EEG grading system; cross-disciplinary method; neonatal brain injury; neurological dysfunction; supervector kernel; support vector machine; Brain models; Electroencephalography; Feature extraction; Pediatrics; Support vector machines; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
  • Conference_Location
    Florence
  • Type

    conf

  • DOI
    10.1109/ICASSP.2014.6854734
  • Filename
    6854734