Discriminant Analysis for Epileptic Seizure Detection

Author

Fathima, Thasneem ; Khan, Yusuf U. ; Bedeeuzzaman, M. ; Farooq, Omar

Author_Institution

Dept. Of Electr. Eng., Aligarh Muslim Univ., Aligarh, India

fYear

2011

fDate

24-25 Feb. 2011

Firstpage

1

Lastpage

5

Abstract

Epilepsy is characterized by the sudden and recurrent neuronal firing in the brain. It can be detected by analyzing Electroencephalogram (EEG) of the subject. In this paper, a method of classification of EEG signals into normal and seizure classes is presented. Features based on the statistical distributions were calculated for each frame of EEG signals. After ranking the features using Fisher´s discriminant analysis variance, skewness and coefficient of variation (CoV) were found to form the best set of features. Classification was done using linear classifier which showed an accuracy of 96.9%.

Keywords

electroencephalography; feature extraction; medical disorders; medical signal detection; medical signal processing; neurophysiology; signal classification; statistical analysis; EEG; Fisher discriminant analysis variance; brain; electroencephalogram; epileptic seizure detection; linear classifier; neuronal firing; signal classification; statistical distributions; variation coefficient; Accuracy; Dispersion; Electroencephalography; Epilepsy; Feature extraction; Histograms; Sensitivity;

fLanguage

English

Publisher

ieee

Conference_Titel

Devices and Communications (ICDeCom), 2011 International Conference on

Conference_Location

Mesra

Print_ISBN

978-1-4244-9189-6

Type

conf

DOI

10.1109/ICDECOM.2011.5738454

Filename

5738454