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

2009

December

Validated

1

Scopus: 8 ()

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.

10.1109/MLSP.2009.5306203