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Background: Juvenile myoclonic epilepsy (JME) is a well-defined idiopathic generalized epileptic syndrome, and diagnostic criteria for JME are to have a normal brain imaging and clinical evidence of typical epileptic seizures. The aim of this study is to evaluate electrophysiological and neuroimaging findings of JME and determine their relationship with prognosis. Methods: Thirty-two patients (23 women and nine men) with a mean age of 22 (16–37) years were included in this study. Interictal electroencephalography (EEG), magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) were carried out in all patients. Results: Analysis of premedication EEGs revealed primary generalized pattern activity in 75% (n=24) and focal abnormalities in 18·75% (n=6). MRI was abnormal in seven (21·88%) patients (two with arachnoid cyst, two with mild cerebral atrophy, two with ventricular enlargement and one with single gliotic lesion), and SPECT imaging detected hypoperfusion in 15 (47%) patients. Hypoperfusion was mostly found on the parietal lobe. Conclusion: We found that, after medication, only 6·25% of EEGs had primary generalized pattern activity (p>0·0001); nevertheless, the prognosis was good in patients who had typical EEG findings (p=0·106). The prognosis of patients with MRI abnormalities was grave (p=0·023). Twenty percent of the patients who had SPECT abnormalities were seizure free, and 80% of them had been partially controlled (p=0·059). There were no correlations between MRI abnormalities, EEG and SPECT findings. [ABSTRACT FROM AUTHOR]

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 subgroups of partial epilepsy by Multilayer Perceptron Neural Networks (MLPNNs). This is the first study to classify the partial epilepsy groups using the neural network according to EEG signals. 418 patients with epilepsy diagnoses according to International League against Epilepsy (ILAE, 1981) were included in this study. The epilepsy outpatients at the Neurology Department Clinic of Cukurova University Medical School between the years of 2002–2005 were examined and included in the study. The MLPNNs were trained by the parameters obtained from the EEG signals and clinical findings of the patients. Test results show that the MLPNN model is able to classify partial epilepsy with an accuracy of 91.5%. Moreover, new MLPNNs were constructed for determining significant variables on classification. The loss of consciousness in the course of seizure time variable caused the largest decrease in the classification accuracy when it was left out. In conclusion, we think that the classification performance of MLPNN model for partial epilepsy is satisfactory and this model may be used in clinical studies as a decision support tool to determine the partial epilepsy classification of the patients. [ABSTRACT FROM AUTHOR]