Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG
Paper i proceeding, 2008

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.

classification

EEG

Burst

Suppression

Neonatal

Författare

Johan Löfhede

Signaler och system, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Johan Degerman

Signaler och system, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Magnus Thordstein

Göteborgs universitet

Anders Flisberg

Göteborgs universitet

Ingemar Kjellmer

Göteborgs universitet

Kaj Lindecrantz

Signaler och system, Signalbehandling och medicinsk teknik, Medicinska signaler och system

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

1557-170X (ISSN)

3836 - 3839

Ämneskategorier

Medicinsk laboratorie- och mätteknik

Fysiologi

Signalbehandling

ISBN

978-1-4244-1814-5