Title :
A Gaussian mixture model based statistical classification system for neonatal seizure detection
Author :
Thomas, Eoin M. ; Temko, Andriy ; Lightbody, Gordon ; Marnane, W.P. ; Boylan, Geraldine B.
Author_Institution :
Dept. of Electr. Eng., UCC, Cork, Ireland
Abstract :
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%.
Keywords :
electroencephalography; feature extraction; medical signal processing; neurophysiology; principal component analysis; signal classification; Gaussian mixture model classifier; feature vector preprocessing; linear discriminant analysis; neonatal seizure detection; postprocessing scheme; principal component analysis; statistical classification system; Artificial neural networks; Detection algorithms; Electroencephalography; Frequency; Independent component analysis; Linear discriminant analysis; Pattern recognition; Pediatrics; Principal component analysis; Support vector machines; Gaussian Mixture Models; Linear Discriminant Analysis; Neonatal Seizure Detection; Principal Component Analysis;
Conference_Titel :
Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4947-7
Electronic_ISBN :
978-1-4244-4948-4
DOI :
10.1109/MLSP.2009.5306203
