Comparison of three methods for classifying burst and suppression in the EEG of post asphyctic newborns.
Fisher's linear discriminant, a feed-forward neural network (NN) and a support vector machine (SVM) are compared with respect to their ability to distinguish bursts from suppression in burst-suppression electroencephalogram (EEG) signals using five features inherent in the EEG as input. The study is based on EEG signals from six full term infants who have suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as area under the curve (AUC) values derived from receiver operating characteristic (ROC) curves for the three methods, and show that the SVM is slightly better than the others, at the cost of a higher computational complexity.
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; D001185:Artificial Intelligence; D001238:Asphyxia Neonatorum / Q000150:complications / Q000175:diagnosis*; D001925:Brain Damage, Chronic / Q000175:diagnosis* / Q000209:etiology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity