EFFECTS OF INPUT DIMENSIONALITY REDUCTION ON THE PERFORMANCE OF EPILEPSY DIAGNOSIS BASED ON NEURAL NETWORK

Epilepsy is a common neurological disorder that is characterized by recurrent unprovoked seizures. About 40 to 50 million people worldwide have epilepsy. In this paper the authors presents clinical decision support system (DSS) for the diagnosis of epilepsy. The DSS is developed by using Multilayer Perceptron (MLP), Generalized Feed Forward Neural Network (GFF-NN) and Elman Neural Network (E-NN). The validity of neural networks to diagnose the epilepsy is checked and the most suitable neural network is recommended for the diagnosis of epilepsy. Also the different feature enhancement techniques like principal component analysis (PCA), FFT and statistical parameters are used for the input dimensionality reduction. Epilepsy diagnosis is modeled as the classification of normal EEG, interictal EEG and ictal EEG. With the different input dimensionality reduction methods performance parameters of MLP, GFF-NN and E-NN are measured and compared. For the GFF-NN, number of free parameter is reduced up to 92.22% when PCA is used for input dimensionality reduction and its overall accuracy of is 98.61%.