Title

Classification of Epileptic EEG Using Neural Network and Wavelet Transform

Abstract

One of the major contributions of electroencephalography has been its application in the diagnosis and clinical evaluation of epilepsy. The interpretation of the EEG is achieved through visual inspection by a trained electroencephalographer. However, descriptions of rules used during the visual analysis of data are often subjective and can vary from one reader to another. Computerized methods are a means to standardize this process. In recent years, much effort has been made to develop such methods that can characterize different interictal, ictal, and postictal stages. the main issue of whether there exists a preictal phenomenon remains unresolved. In the present study we address this issue making use of specifically designed and trained recurrent neural networks in conjunction with signal wavelet decomposition technique. The purpose of this combined consideration was to demonstrate the potential for seizure prediction by up to several minutes prior to its onset.

Meeting Name

Wavelet Applications in Signal and Image Processing IV (1996: Aug. 6, Denver, CO)

Department(s)

Electrical and Computer Engineering

International Standard Serial Number (ISSN)

0277-786X

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1996 SPIE--The International Society for Optical Engineering, All rights reserved.


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