Classification of burst and suppression in the neonatal electroencephalogram
Journal article, 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.

Author

Johan Löfhede

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

Nils Löfgren

University of Borås

Magnus Thordstein

University of Gothenburg

Anders Flisberg

University of Gothenburg

Ingemar Kjellmer

University of Gothenburg

Kaj Lindecrantz

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

Journal of Neural Engineering

1741-2560 (ISSN)

Vol. 5 4 402-10

Subject Categories

Industrial Biotechnology

Physiology

Other Social Sciences not elsewhere specified

DOI

10.1088/1741-2560/5/4/005

PubMed

18971517

More information

Latest update

3/8/2018 9