Automatic detection of epileptic seizure using dynamic fuzzy neural networks

In this study, a new approach based on neural network and fuzzy logic technologies was presented for detection of epileptic seizure to allow for the incorporation of both heuristics and deep knowledge to exploit the best characteristics of each. A dynamic fuzzy neural network (DFNN) that contains dynamical elements in their processing units is used in the classification of EEG signals. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a DFNN with two discrete outputs: normal and epileptic. Some conclusions concerning the impacts of features on epileptic seizure detection was obtained through analysis of the DFNN. The performance of the DFNN model was evaluated in terms of classification accuracies and the results confirmed that the proposed DFNN classifiers have some potential in detecting epileptic seizures. The DFNN model achieved accuracy rates, which were higher than that of neural network model.