Classification of burst and suppression in the neonatal electroencephalogram
Artikel i vetenskaplig tidskrift, 2008

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.


Johan Löfhede

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Nils Löfgren

Högskolan i Borås

Magnus Thordstein

Göteborgs universitet

Anders Flisberg

Göteborgs universitet

Ingemar Kjellmer

Göteborgs universitet

Kaj Lindecrantz

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Journal of Neural Engineering

1741-2560 (ISSN)

Vol. 5 4 402-10


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