• DocumentCode
    2375254
  • Title

    Age-independent seizure detection

  • Author

    Faul, Stephen ; Temko, Andriy ; Marnane, William

  • Author_Institution
    Dept. of Electr. Eng., Univ. Coll. Cork, Cork, Ireland
  • fYear
    2009
  • fDate
    3-6 Sept. 2009
  • Firstpage
    6612
  • Lastpage
    6615
  • Abstract
    This paper examines whether an appropriate algorithm, developed for use with neonatal data, could also be used, without alteration, for the detection of seizures in adults with epilepsy. The performance of a feature extraction and SVM classifier system is evaluated on databases of 17 neonatal patients and 15 adult patients. Mean ROC curve areas of 0.96 and 0.94 for neonatal and adult databases respectively show that high accuracy can be achieved independent of age. It is also shown that features contribute differently for neonatal and adult data.
  • Keywords
    electroencephalography; feature extraction; neurophysiology; patient diagnosis; support vector machines; SVM classifier; age independent seizure detection; epilepsy; feature extraction; neonatal data; Adolescent; Adult; Aging; Algorithms; Artifacts; Electroencephalography; Humans; Infant, Newborn; ROC Curve; Seizures; Young Adult;
  • 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.2009.5332553
  • Filename
    5332553