期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2011
卷号：1
期号：4
页码：21-27
出版社：International Journal of Soft Computing & Engineering
摘要：The purpose of this research is to investigate the feasibility of Game theory based Max-Min optimization of fuzzy outputs for the classification of epilepsy risk levels 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. Max-Min SDT (Soft Decision Tree) as post classifier with four methods is applied on the classified data to identify the optimized risk level (singleton) that 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 Quality Value (QV). A group of ten patients with known epilepsy findings are used for this study. High PI such as 94.56 % was obtained at QV’s of 22.42 in the SDT optimization when compared to the value of 40% and 6.25 through fuzzy classifier respectively. We identified that the SDT provides a better performing tool for optimizing the epilepsy risk levels
