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Journal of Biological Sciences
  Year: 2008 | Volume: 8 | Issue: 2 | Page No.: 451-455
DOI: 10.3923/jbs.2008.451.455
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Diagnosis of Epilepsy By Artificial Neural Network

M.I. EL-Gohary, A.S.A. Mohamed, M.M. Dahab, M.A. Ibrahim, A.A. El-Saeid and H.A. Ayoub

A common feature of epilepsy in EEG signals is an excessive electrical discharge which is appeared as electrical potentials of high amplitudes and frequencies with abrupt onset and rise in amplitude, rythmicity and abnormal synchronization. These potential discharges were termed Seizure patterns. Although several details concerning the cellular basis of these seizure patterns are unknown, numerous experiments led to the general agreement that they reflect a spontaneous and uncontrolled firing of a large number of neurons within a certain region of the brain. Artificial Neural Network (ANN) was proposed in this research as a decision-making tool supported by experimental data to differentiate between healthy and epileptic EEG signals, with accuracy up to 90.2%. This was done by teaching the ANN to perform this function i.e., by Artificial Intelligence (AI) of ANN. The performance of the ANN was calculated for each model`s node to obtain the performance of the node. ANN approach is a powerful tool which is promising to give available results in analysis of bioelectric signals.
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How to cite this article:

M.I. EL-Gohary, A.S.A. Mohamed, M.M. Dahab, M.A. Ibrahim, A.A. El-Saeid and H.A. Ayoub, 2008. Diagnosis of Epilepsy By Artificial Neural Network. Journal of Biological Sciences, 8: 451-455.

DOI: 10.3923/jbs.2008.451.455






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