Comparing a supervised and an unsupervised classification method for burst detection in neonatal EEG.
Hidden Markov Models (HMM) and Support Vector Machines (SVM) using unsupervised and supervised training, respectively, were compared with respect to their ability to correctly classify burst and suppression in neonatal EEG. Each classifier was fed five feature signals extracted from EEG signals from six full term infants who had suffered from perinatal asphyxia. Visual inspection of the EEG by an experienced electroencephalographer was used as the gold standard when training the SVM, and for evaluating the performance of both methods. The results are presented as receiver operating characteristic (ROC) curves and quantified by the area under the curve (AUC). Our study show that the SVM and the HMM exhibit similar performance, despite their fundamental differences.
Löfhede
J
J
School of Engineering, University College of Boraş, Sweden. johan.lofhede@hb.se
Degerman
J
J
Löfgren
N
N
Thordstein
M
M
Flisberg
A
A
Kjellmer
I
I
Lindecrantz
K
K
D000465:Algorithms; D004569:Electroencephalography / Q000145:classification* / Q000706:statistics & numerical data*; D006801:Humans; D007231:Infant, Newborn / Q000502:physiology*; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011336:Probability; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted