Analysis the EEG Signal to Detect Epilepsy Using Artificial Neural Network

ELNAZ, NOMIGOLZAR and MANSOUR, ESMAEILPOUR and ESMAEILPOUR, NADERIFAR (2014) Analysis the EEG Signal to Detect Epilepsy Using Artificial Neural Network. In: Second International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2014, 20 - 21 December, 2014, Kuala Lumpur, Malaysia.

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According to the World Health Statistics, the epilepsy is a disease that suffer about one percent of people in the world. The EEG Signals as electrical activity of the brain use to detect type of epilepsy. Epilepsy will be detected by the recurrence of epileptic seizures in EEG signals. In most cases, it can not predict the onset in a short period, but requires a continuous recording of the EEG signal. Conventional way of recording tape that has been recorded for this method is mobile that keeps the EEG data for a very long time, even up to a week holds. Since conventional methods of analysis are very tedious and time consuming, EEG automatic seizure detection methods have been developed in recent years, but the error percentage of them is high. Therefore, this paper presents a method based on artificial neural network for detecting the epilepsy that results demonstrate, good accuracy of the proposed model.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Epilepsy, Artificial Neural Network, EEG Signal
Depositing User: Mr. John Steve
Date Deposited: 22 Apr 2019 11:46
Last Modified: 22 Apr 2019 11:46

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