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Authors

Thomas, EM;Temko, A;Lightbody, G;Marnane, WP;Boylan, GB

Conference Title

2009 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING

Title of Paper

A Gaussian mixture model based statistical classification system for neonatal seizure detection

Year

2009

Month

January

Status

Validated

Peer Reviewed

Times Cited

()

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EEG

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Start Page

446

End Page

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End Date

Abstract

A neonatal seizure detection system is proposed based on a Gaussian mixture model classifier. Linear discriminant analysis and principal component analysis are compared for the task of feature vector preprocessing. A postprocessing scheme is developed from the probability of seizure estimate in order to improve the performance of the system. Results are reported on a dataset of 17 patients with a total duration of 267.9 hours, the average ROC area of the system is 95.6%.

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