Performance Analysis of Singular Value Decomposition (SVD) and Radial basis Function (RBF) Neural Networks for Epilepsy Risk Levels Classifications from EEG Signals

The objective of this paper is to compare the performance of Singular Value Decomposition (SVD) method and Radial Basis Function (RBF) Neural Network for optimization of fuzzy outputs in the epilepsy risk level classifications from EEG (Electroencephalogram) signals. The fuzzy pre classifier is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. SVD and RBF neural network is exploited on the classified data to identify the optimized risk level (singleton) which characterizes the patient’s epilepsy risk level. The efficacy of the above methods is compared based on the bench mark parameters such as Performance Index (PI), and