Detection of bursts in the EEG of post asphyctic newborns.
Eight features inherent in the electroencephalogram (EEG) have been extracted and evaluated with respect to their ability to distinguish bursts from suppression in burst-suppression EEG. The study is based on EEG from six full term infants who had suffered from lack of oxygen during birth. The features were used as input in a neural network, which was trained on reference data segmented by an experienced electroencephalographer. The performance was then evaluated on validation data for each feature separately and in combinations. The results show that there are significant variations in the type of activity found in burst-suppression EEG from different subjects, and that while one or a few features seem to be sufficient for most patients in this group, some cases require specific combinations of features for good detection to be possible.
Löfhede
J
J
Sch. of Eng., Univ. Coll. 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 / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity