Classification of Epileptic EEG Using Neural Network and Wavelet Transform
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.
A. A. Petrosian et al., "Classification of Epileptic EEG Using Neural Network and Wavelet Transform," Proceedings of SPIE - The International Society for Optical Engineering, vol. 2825, pp. 834-843, SPIE--The International Society for Optical Engineering, Jan 1996.
The definitive version is available at https://doi.org/10.1117/12.255307
Wavelet Applications in Signal and Image Processing IV (1996: Aug. 6, Denver, CO)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 1996 SPIE--The International Society for Optical Engineering, All rights reserved.