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

Eoin M. Thomas, Andriy Temko, Gordon Lightbody, William P. Marnane, Geraldine B. Boylan
2009 2009 IEEE International Workshop on Machine Learning for Signal Processing  
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%.
doi:10.1109/mlsp.2009.5306203 fatcat:eho5qouoezcmnihnskzm6vo5ie