150. DETECTION OF EPILEPSY USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM
This study presents the use of Adaptive Neuro-Fuzzy Inference System (ANFIS) for classification of the EEG signals. The data consists of two types of EEG signals, i.e. epileptic patients during epilepsy and healthy patients when their eyes are open. The proposed algorithm has several steps. First, in order to remove the artefacts (filter the signals) we use band-pass Finite Impulse Response (FIR) filtering with the Hamming window. Feature extraction is made in the second step, using Discrete Wavelet Transform (DWT) and statistical analysis. In this way we reduce the dimensionality of the input data, lately used as input parameters in the ANFIS network. ANFIS model learns how to classify the EEG signal, through the standard hybrid learning algorithm. We use special form of ANFIS model, which depending on the number of inputs, splits the model into appropriate number of substructures (sub-ANFIS models). ANFIS model was evaluated in terms of training performance and classification accuracies. From the simulation results it was concluded that the proposed algorithm has good potentials in classifying the EEG signals.