Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
BIONET: an artificial neural network model for diagnosis of diseases
Pattern Recognition Letters
An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs
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
Journal of Medical Systems
A Radial Basis Function Neural Network Model for Classification of Epilepsy Using EEG Signals
Journal of Medical 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 by using Multi-Layer Perceptron Neural Networks (MLPNNs). 418 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 MLPNNs. The MLPNNs were trained by the parameters obtained from the EEG signals and clinic properties of the patients. We classified the epilepsy into two groups such as partial and primary generalized epilepsy and we achieved an 89.2% correct prediction rate by using MLPNN model. The parameters of the loss of consciousness in the course of seizure, the duration and ritmicity of abnormal activities found in EEG constituted the most significant variables in the classification of epilepsy by using MLPNN. These results indicate that the classification performance of MLPNN model for epilepsy groups is satisfactory and we think that this model may be used in clinical studies as a decision support tool to confirm the classification of epilepsy groups after they are developed.