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Classifying burst and suppression in the EEG of post asphyctic newborns using a support vector machine
School of Engineering, University of Borås.
School of Engineering, University of Borås.
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: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, IEEE , 2007, 630-633 p.Conference paper (Refereed)
Abstract [en]

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.
Keyword [en]
Animals, Data acquisition, Electroencephalography, Optimization, Patient treatment, Electroencephalographers, Support vector machines
National Category
Medical Engineering
URN: urn:nbn:se:kth:diva-75387DOI: 10.1109/CNE.2007.369752ISI: 000248513500157ISBN: 978-1-4244-0791-0OAI: diva2:490721
3rd International IEEE/EMBS Conference on Neural Engineering Location: Kohala Coast, HI Date: MAY 02-05, 2007
QC 20120219Available from: 2012-02-06 Created: 2012-02-05 Last updated: 2012-02-19Bibliographically approved

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Lindecrantz, Kaj
Medical Engineering

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ReferencesLink to record
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