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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