Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
Journal of Biological Sciences
Year: 2008  |  Volume: 8  |  Issue: 2  |  Page No.: 451 - 455

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    

Abstract: 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.

Cited References   |    Fulltext    |   Related Articles   |   Back
 
 
   
 
 
 
  Related Articles

 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility