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Comparison of three methods for classifying burst and suppression in the EEG of post asphyctic newborns
School of Engineering, University of Borås.
Neoventa Medical AB, Göteborg.
Department of Clinical Neurophysiology, Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, Göteborg, Sweden.
Department of Pediatrics, Queen Silvia Children's Hospital, Sahlgrenska University Hospital-Östra.
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2007 (English)In: 2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16, IEEE , 2007, Vol. 2007, 5136-5139 p.Conference paper (Refereed)
Abstract [en]

Fisher's linear discriminant, a feed-forward neural network (NN) and a support vector machine (SVM) are compared with respect to their ability to distinguish bursts from suppression in burst-suppression electroencephalogram (EEG) signals using five features inherent in the EEG as input. The study is based on EEG signals from six full term infants who have suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as area under the curve (AUC) values derived from receiver operating characteristic (ROC) curves for the three methods, and show that the SVM is slightly better than the others, at the cost of a higher computational complexity.

Place, publisher, year, edition, pages
IEEE , 2007. Vol. 2007, 5136-5139 p.
, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, ISSN 1094-687X
National Category
Medical Engineering
URN: urn:nbn:se:kth:diva-75342DOI: 10.1109/IEMBS.2007.4353496ISI: 000253467004018PubMedID: 18003162ISBN: 978-1-4244-0787-3OAI: diva2:490483
29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07; Lyon; 23 August 2007 through 26 August 2007
QC 20120219Available from: 2012-02-05 Created: 2012-02-05 Last updated: 2012-02-19Bibliographically approved

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Lindecrantz, Kaj
Medical Engineering

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