Thomas, E.M., Temko, A., Lightbody, G., Marnane, W.P. and Boylan, G.B ;

2009 IEEE International Workshop on Machine Learning For Signal Processing (MLSP2009)

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

2009

September

Published

1

()

Neonatal Seizure Detection, Linear Discriminant Analysis, Principal Component Analysis, Gaussian Mixture Models.

1

6

Grenoble, France

02-SEP-09

04-SEP-09

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%.

10.1109/WISP.2009.5286564

Science Foundation Ireland

05/PICAlI836