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Classification of burst and suppression in the neonatal electroencephalogram
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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2008 (English)In: Journal of Neural Engineering, ISSN 1741-2560, Vol. 5, no 4, 402-410 p.Article in journal (Refereed) Published
Abstract [en]

Fisher's linear discriminant (FLD), a feed-forward artificial neural network (ANN) and a support vector machine (SVM) were compared with respect to their ability to distinguish bursts from suppressions in electroencephalograms (EEG) displaying a burst-suppression pattern. Five features extracted from the EEG were used as inputs. The study was based on EEG signals from six full-term infants who had suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as the area under the curve (AUC), derived from receiver operating characteristic (ROC) curves for the three methods. Based on this, the SVM performs slightly better than the others. Testing the three methods with combinations of increasing numbers of the five features shows that the SVM handles the increasing amount of information better than the other methods.

Place, publisher, year, edition, pages
2008. Vol. 5, no 4, 402-410 p.
National Category
Pediatrics Other Medical Engineering
URN: urn:nbn:se:kth:diva-75341DOI: 10.1088/1741-2560/5/4/005ISI: 000262020400005PubMedID: 18971517OAI: diva2:490698
QC 20120219Available from: 2012-02-06 Created: 2012-02-05 Last updated: 2012-02-19Bibliographically approved

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Lindecrantz, Kaj
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