Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs
Journal of Medical Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Neural Network Approach in Diabetes Management by Insulin Administration
Journal of Medical Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Classification of MCA Stenosis in Diabetes by MLP and RBF Neural Network
Journal of Medical Systems
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features
Journal of Medical Systems
Is Levenberg-Marquardt the Most Efficient Optimization Algorithm for Implementing Bundle Adjustment?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The application of neural networks in classification of epilepsy using EEG signals
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Robust radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
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Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient's epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to evaluate epileptic patients and classify epilepsy groups such as partial and primary generalized epilepsy by using Radial Basis Function Neural Network (RBFNN) and Multilayer Perceptron Neural Network (MLPNNs). Four hundred eighteen patients with epilepsy diagnoses according to International League against Epilepsy (ILAE 1981) were included in this study. The correct classification of this data was performed by two expert neurologists before they were executed by neural networks. The neural networks were trained by the parameters obtained from the EEG signals and clinic properties of the patients. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. According to test results, RBFNN (total classification accuracy驴=驴95.2%) has classified more successfully when compared with MLPNN (total classification accuracy驴=驴89.2%). These results indicate that RBFNN model may be used in clinical studies as a decision support tool to confirm the classification of epilepsy groups after the model is developed.