Epileptic seizure detection using neural fuzzy networks
Sadati N., Mohseni HR., Maghsoudi A.
The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnosis. The aim of this work is to compare the different classifiers when applied to EEG data from normal and epileptic subjects. For this purpose an adaptive neural fuzzy network (ANFN) to classify normal and epileptic EEG signals is proposed. The results are compared with other classifiers such as SVM (Support Vector Machine), ANFIS and FBNN (Feed forward Back-propagation Neural Network). It is shown that a classification accuracy of about 85.9% can be achieved using ANFN. © 2006 IEEE.