Comparison of hierarchical aggregation functions decision Trees and Rule Based AI optimization in the classification of fuzzy based epilepsy risk levels from EEG signals

The objective of this paper is to compare the performance of Hierarchical Soft Decision Trees and Rule based AI techniques in 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. Hierarchical Soft decision tree (post classifiers with max-min criteria) four types and AI optimization are 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).