Gaussian mixture models for classification of neonatal seizures using EEG

A real-time neonatal seizure detection system is proposed based on a Gaussian mixture model classifier. The system includes feature transformation techniques and classifier output postprocessing. The detector was evaluated on a database of 20 patients with 330 h of recordings. A detailed analysis of...

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Bibliographic Details
Published inPhysiological measurement Vol. 31; no. 7; pp. 1047 - 1064
Main Authors Thomas, E M, Temko, A, Lightbody, G, Marnane, W P, Boylan, G B
Format Journal Article
LanguageEnglish
Published England IOP Publishing 01.07.2010
Subjects
Classification
Classifiers
Detectors
Electrodes
Electroencephalography - methods
False Positive Reactions
Gaussian
Humans
Infant, Newborn
Models, Neurological
Movement
Normal Distribution
Recording
ROC Curve
Seizures
Seizures - classification
Signal Processing, Computer-Assisted
Transformations
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Summary:A real-time neonatal seizure detection system is proposed based on a Gaussian mixture model classifier. The system includes feature transformation techniques and classifier output postprocessing. The detector was evaluated on a database of 20 patients with 330 h of recordings. A detailed analysis of the choice of parameters for the detector is provided. A mean good detection rate of 79% was obtained with only 0.5 false detections per hour. A thorough review of all misclassified events was performed, from which a number of patterns causing false detections were identified.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0967-3334
1361-6579
DOI:10.1088/0967-3334/31/7/013