Neural network-based computer-aided diagnosis in classification of primary generalized epilepsy by EEG signals.
J Med Syst
; 33(2): 107-12, 2009 Apr.
Article
en En
| MEDLINE
| ID: mdl-19397095
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 classify subgroups of primary generalized epilepsy by using Multilayer Perceptron Neural Networks (MLPNNs). This is the first study classifying primary generalized epilepsy using MLPNNs. MLPNN classified primary generalized epilepsy with the accuracy of 84.4%. This model also classified generalized tonik-klonik, absans, myoclonic and more than one type seizures epilepsy groups correctly with the accuracy of 78.5%, 80%, 50% and 91.6%, respectively. Moreover, new MLPNNs were constructed for determining significant variables affecting the classification accuracy of neural networks. The loss of consciousness in the course of seizure time variable caused the largest decrease in the classification accuracy when it was left out. These outcomes indicate that this model classified the subgroups of primary generalized epilepsy successfully.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Diagnóstico por Computador
/
Redes Neurales de la Computación
/
Epilepsia Generalizada
/
Electroencefalografía
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Adolescent
/
Adult
/
Child
/
Child, preschool
/
Female
/
Humans
/
Infant
/
Male
/
Middle aged
/
Newborn
País/Región como asunto:
Asia
Idioma:
En
Revista:
J Med Syst
Año:
2009
Tipo del documento:
Article
País de afiliación:
Turquía