Epilepsy detection on EEG data using backpropagation, firefly algorithm and simulated annealing

Abstract

Epilepsy is a central system disorder of human brain in which nerve cell activity becomes disrupted, causing seizures or periods of unsual behaviour, sensations and sometimes loss of consciousness. The electroencephalogram (EEG) is a measure of brain waves can be used in evaluation of brain disorders, one of which epilepsy. In this paper, epilepsy detection system on EEG data is built using combination of backpropagation and simulated annealing. Firefly algorithm and simulated annealing are used to determine optimal learning rate and number of unit hidden on backpropagation process. Then learning rate and number of unit hidden are used for trainning and validation backpropagation testing process on EEG data epilepsi detection. The percentages of success rate detection epilepsy EEG data obtained for 93.3% using the learning rate 0.93 and the number of hidden layer units as much as 7 to mean square error of 0.00535.