PERFORMANCE ANALYSIS OF PSO AS POST CLASSIFIER IN DETECTION OF EPILEPSY RISK LEVELS FROM EEG SIGNALS

The objective of this paper is to analyze the performance of Particle Swarm Optimization (PSO) in optimization of code converter outputs for the classification of epilepsy risk levels from EEG (Electroencephalogram) 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. PSO 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 76.3 % was obtained at QV's of 19.8, for PSO optimization with rigrsure Soft Thresholding when compared to the value of 40% and 12.25 through code converter classifier respectively.