Estimation of epileptic seizure by using Lyapunov exponent, wavelet entropy and Artificial Neural Networks

Author

Acar, Hüseyin ; BAYRAM, Muhittin

Author_Institution

Elektrik - Elektron. Muhendisligi Bolumu, Dicle Univ., Diyarbakr, Turkey

fYear

2012

fDate

18-20 April 2012

Firstpage

1

Lastpage

4

Abstract

Brain signals are widely used for diagnosing epilepsy disease. The objective of this study is to design an automated system for differentiating epileptic EEG signals from non epileptic ones. The EEG signals used in the study comprise both healthy and epileptic signals which have been taken from patients during seizure. The signals were analyzed in phase space by means of Lyapunov exponent and wavelet entropy. Some features were identified from this phase space data and automatically classified by an adapted Artificial Neural Networks (ANN).

Keywords

electroencephalography; entropy; medical signal processing; neural nets; wavelet transforms; ANN; Lyapunov exponent; artificial neural networks; automated system; brain signals; epilepsy disease diagnosis; epileptic EEG signals; epileptic seizure estimation; healthy signals; phase space data; wavelet entropy; Artificial neural networks; Brain modeling; Electroencephalography; Entropy; Epilepsy; Humans; Wavelet analysis; ANN; EEG; Lyapunov exponent; epilepsy; wavelet entropy;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2012 20th

Conference_Location

Mugla

Print_ISBN

978-1-4673-0055-1

Electronic_ISBN

978-1-4673-0054-4

Type

conf

DOI

10.1109/SIU.2012.6204614

Filename

6204614