Comparison of Three Methods for Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns
Paper in proceedings, 2007

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.

Male

Artificial Intelligence

Sensitivity and Specificity

Asphyxia Neonatorum

methods

Chronic

Reproducibility of Results

etiology

Newborn

methods

diagnosis

methods

complications

Algorithms

Classification

Pattern Recognition

Diagnosis

Humans

Brain Damage

Computer-Assisted

Automated

Electroencephalography

Infant

Biomedical Signal Processing

diagnosis

Author

Johan Löfhede

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

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, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

1557-170X (ISSN)

5136 - 5139

Subject Categories

Medical Laboratory and Measurements Technologies

Physiology

ISBN

978-1-4244-0788-0

More information

Created

10/8/2017