NDLI: A Radial Basis Function Neural Network Model for Classification of Epilepsy Using EEG Signals

Content Provider	Springer Nature Link
Author	Aslan, Kezban Bozdemir, Hacer Şahin, Cenk Oğulata, Seyfettin yan Erol, Rızvan
Copyright Year	2008
Abstract	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 = 95.2%) has classified more successfully when compared with MLPNN (total classification accuracy = 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.
Starting Page	403
Ending Page	408
Page Count	6
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	32
Issue Number	5
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2008-04-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Epilepsy EEG Radial basis function (RBF) Multilayer perceptron (MLP) Neural network Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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