The Detection of Normal and Epileptic EEG Signals using ANN Methods with Matlab-based GUI

Epilepsy is common neurological disorder disease in the world. Electroencephalogram (EEG) can provide significant information about epileptic activity in human brain. Since detection of the epileptic activity requires analyzing of very length EEG recordings by an expert, researchers tend to improve automated diagnostic systems for epilepsy in recent years. In this work, we try to automate detection of epilepsy using EEG based on Matlab Graphical User Interface (GUI). Three different types of Artificial Neural Networks (ANN), namely, Feed Forward Backpropagation, Cascade and Elman neural networks, are used for the classification EEG (existence of epileptic seizure or not). Before classification process, we use autoregressive model to data reduction and three different AR model algorithms to calculate the coefficients. Developed Matlab-based GUI provides flexible and visual utilization to observe normal/epileptic EEG and test results. Training parameters and type of neural networks are decided by users on the interface. Performance of the proposed model is evaluated using overall accuracy.