Classification of burst and suppression in the neonatal electroencephalogram.
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.
Löfhede
J
J
School of Engineering, University College of Borås, Borås, Sweden. johan.lofhede@hb.se
Löfgren
N
N
Thordstein
M
M
Flisberg
A
A
Kjellmer
I
I
Lindecrantz
K
K
D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001238:Asphyxia Neonatorum / Q000175:diagnosis* / Q000503:physiopathology; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D004569:Electroencephalography / Q000145:classification* / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn / Q000502:physiology*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012372:ROC Curve; D015203:Reproducibility of Results