Conference Publication Details
Mandatory Fields
Thomas E.;Temko A.;Lightbody G.;Marnane W.;Boylan G.
Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
A Gaussian mixture model based statistical classification system for neonatal seizure detection
Scopus: 8 ()
Optional Fields
Gaussian mixture models Linear discriminant analysis Neonatal seizure detection Principal component analysis
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%. 2009 IEEE.
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