Classifying burst and suppression in the EEG of post asphyctic newborns using a support vector machine
2007 (English)In: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, IEEE , 2007, 630-633 p.Conference paper (Refereed)
A Support Vector Machine (SVM) was trained to distinguish bursts from suppression in burst-suppression EEG, using Ave features inherent in the electro-encephalogram (EEG) as input. The study was based on data from six full term infants who had suffered from perinatal asphyxia, and the machine was trained with reference classifications made by an experienced electroencephalographer. The results show that the method may be useful, but that differences between patients in the data set makes optimization of the system difficult.
Place, publisher, year, edition, pages
IEEE , 2007. 630-633 p.
Animals, Data acquisition, Electroencephalography, Optimization, Patient treatment, Electroencephalographers, Support vector machines
IdentifiersURN: urn:nbn:se:kth:diva-75387DOI: 10.1109/CNE.2007.369752ISI: 000248513500157ISBN: 978-1-4244-0791-0OAI: oai:DiVA.org:kth-75387DiVA: diva2:490721
3rd International IEEE/EMBS Conference on Neural Engineering Location: Kohala Coast, HI Date: MAY 02-05, 2007
QC 201202192012-02-062012-02-052012-02-19Bibliographically approved