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

Author

Johan Löfhede

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

Johan Degerman

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Magnus Thordstein

University of Gothenburg

Anders Flisberg

University of Gothenburg

Ingemar Kjellmer

University of Gothenburg

Kaj Lindecrantz

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

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

1557-170X (ISSN)

3836 - 3839

Subject Categories

Medical Laboratory and Measurements Technologies

Physiology

Signal Processing

ISBN

978-1-4244-1814-5

More information

Created

10/8/2017