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Proceedings Paper

Classification of epileptic EEG using neural network and wavelet transform
Author(s): Arthur Ashot Petrosian; Richard Homan; Danil Prokhorov; Donald C. Wunsch II
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Paper 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.

Paper Details

Date Published: 23 October 1996
PDF: 10 pages
Proc. SPIE 2825, Wavelet Applications in Signal and Image Processing IV, (23 October 1996); doi: 10.1117/12.255307
Show Author Affiliations
Arthur Ashot Petrosian, Texas Tech Univ. (United States)
Richard Homan, Texas Tech Univ. (United States)
Danil Prokhorov, Texas Tech Univ. (United States)
Donald C. Wunsch II, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 2825:
Wavelet Applications in Signal and Image Processing IV
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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