Epilepsy is a neurological issue that is realized by a ceaseless variety from the standard of cerebrum discharge. Checking mind development through electroencephalography (EEG) has transformed into a noteworthy gadget for the assurance of epilepsy. EEG accounts in epileptic patients show two sorts of bizarre activity: sporadic sign recorded during the epileptic seizure; and seizures, practices recorded during the epileptic seizure. The guideline target of our investigation is to separate the picked up EEG sign using sign planning instruments, (for instance, wavelet changes) and request them into different classifications. The features from the EEG are isolated using quantifiable assessment of parameters procured by wavelet change. Complete 300 EEG data subjects were bankrupt down. These data were collected in three classes' i.e, Normal patient class, Epileptic patient class and epileptic patient during non-seizure zone independently. In order to achieve this we have associated a back expansion based neural network classifier. After feature extraction discretionary goal is to improve the precision of Classification.100 subjects from each set were destitute down for feature extraction and classification and data were divided in getting ready, testing and endorsement of proposed estimation.
EEG, Epilepsy, Wavelet transform, Neural network
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