Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures

Simulations results showed that the tan sigmoid, pure linear were found to be optimal input-hidden, hidden-output transfer function and Levenberg-Marquardt learning algorithm as optimal training function for all the four classification tasks. It was inferred from the proposed study that the CA indirectly varies with γ value, p value, and MSE, and directly varies with z-score. From the experimental study, the best CA of 97.68%, 94.56%, 84.58%, and 57.8% was obtained for case CE, DE, CDE, and CD respectively. It can be concluded that proposed features with optimally configured MLP-NN found to be helpful for real-time iEEG classification.