Performance analysis of wavelet transforms and principal components as post classifier for the classification of epilepsy risk levels from EEG signals

Abstract

The Objective of this paper is to analyze the performance of Principal components in optimization of code converter outputs in the classification of epilepsy risk levels from Electroencephalogram (EEG) signals. The Code converter 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. Principal component method is applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient's risk level. The efficacy of the above methods is compared based on the bench mark parameters such as Performance Index (PI), and Quality Value (QV). A group of twenty patients with known epilepsy findings are analyzed. High PI such as 97.35 % was obtained at QV's of 23.2, for Principal component optimization when compared to the value of 40 % and 6.25 through code converter classifier respectively. It was identified that Principal Component Method is a good post classifier in the optimization of epilepsy risk levels. © 2013 Springer.