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Background: Epileptic seizures are manifestation of epilepsy. Understanding of the mechanisms causing epileptic disorder needs careful analyses of the electroencephalograph (EEG) records. The detection of epileptic form discharges (spike wave) in the EEG is an important component in the diagnosis of epilepsy. Approximately one in every 100 persons will experience a seizure at some time in their life. Already intelligence spike detection method discucsed but purpose of this research is diagnosis of different kind of epilepsy (grandmal and Petitmal) by design of an intelligence diagnosis processing. Methods and Materials: In this descriptive study, 100 EEG signals of brain hemispheres from different person in healthy, interictal and ictal conditions were used. Fifty Hz noise and artifact signals were removed by soft ware procedure then signals separated by expert neurologist to three categories, healthy (frequency band 8-12 Hz), petitmal seizures (typical 3 Hz), grandmal seizures (clonic stage with 4 Hz frequency) and divided each of them to 6 seconds segments. Information of this signals (background alpha, spike and slow, poly spike and poly sharp) were extracted by wavelet transform and classified by soft ware procedure neural network to there groups healthy, ptitmal and grandmal epilepsy. Results: In designed software accuracy of diagnosis ptitmal and grandmal epilepsies was obtained about 80% Conclusion: This method introduced intelligent diagnosis of epilepsy (ptitmal and gradmal) and automatically detected healthy person from epileptic patients. One of the other advantages is help to neurologist for detection of sickness clearly and expendable different kinds of other epilepsy

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Background: The general method for paraclinic diagnosis of epilepsy is electroencephalography that is performed by visual analysis by experienced neurologist. However, due to false detection and impossibility of evaluating electrodes and brain areas coherence, it is not uniquely used for seizure detection. In recent years, Quantitative Electroencephalogram (QEEG) has become a strong instrument for detection of brain disorders. Hence, studies in the field of EEG performance improvement and brain mapping images analysis corresponding to new methods that contain 2-D and 3-D output images and automatic epilepsy diagnosis are necessary.

Materials and Methods: In this cross-sectional study, through extracting epilepsy feature by computing the energy of each EEG channel, brain map pattern of each patient was plotted using cubic interpolation and generalized and partial patterns and potential center of epilepsy were diagnosed by LVQ artificial neural network using image processing combination methods.

Results: In the proposed algorithm, 11 epilepsy brain mapping patterns, including 1 generalized and 10 partial seizure patterns, were automatically diagnosed.

Conclusion: Since seizure detection in the EEG signals is a complex procedure and the number of expert neurologists is small, this schema can be used for epilepsy diagnosis as an intelligent diagnosis method so that generalization of this method can help detect various brain disorders.