Application of Neural Networks to the Electroencephalogram Analysis for Epilepsy Detection

Many techniques were used in order to detect and to predict epileptic seizures on the basis of electroencephalograms. One of the approaches for the prediction of the epileptic seizures is the use the chaos theory, namely determination largest Lyapunov's exponent or correlation dimension of the...

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Bibliographic Details
Published in2007 International Joint Conference on Neural Networks pp. 2707 - 2711
Main Authors Golovko, V.A., Bezobrazova, S.V., Bezobrazov, S.V., Rubanau, U.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Artificial intelligence
Biological neural networks
Chaos
Computer networks
Electroencephalography
Epilepsy
Neural networks
Nonlinear equations
Scalp
Signal processing
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Summary:Many techniques were used in order to detect and to predict epileptic seizures on the basis of electroencephalograms. One of the approaches for the prediction of the epileptic seizures is the use the chaos theory, namely determination largest Lyapunov's exponent or correlation dimension of the scalp EEG signals. This paper presents the neural network technique for epilepsy detection. It is based on computing of the largest Lyapunov's exponent. The results of experiments are discussed.
ISBN:9781424413799
1424413796
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2007.4371386