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

fYear

2009

fDate

1-4 Sept. 2009

Firstpage

1

Lastpage

6

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/MLSP.2009.5306203

Filename

5306203