Thesis:

Prediction and classification of epileptic seizures by the method of adaptive neural fuzzy inference system

The purpose of this research is to predict and classify convulsions using the method of adaptive neural fuzzy inference system. In this research, due to the necessity of early and timely diagnosis of this disease, artificial neural fuzzy algorithms were used in order to adapt . learning and classification Many researchers have a great desire to use this tool, but the challenge of training or choosing the right input according to the time series or the behavior of the EEG report is the most important part of determining the inputs of the neural fuzzy network algorithm, especially because of the limitation in applying the input compared to the evolutionary algorithms. Fuzzy logic of data mining systems and neural networks can be a suitable idea to increase the efficiency of these networks in order to detect and select the feature (delay) that ultimately leads to the correct performance and classification of data. The most important issue in using the ANFIS algorithm or neural fuzzy networks is the discussion of creating a fuzzy structure and training the network.

Keywords: Prediction, classification, epileptic seizures, adaptive neural fuzzy inference