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

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5 Author(s)
Thomas, E.M. ; Dept. of Electr. Eng., UCC, Cork, Ireland ; Temko, A. ; Lightbody, G. ; Marnane, W.P.
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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%.

Published in:

Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on

Date of Conference:

1-4 Sept. 2009