Fuzzy techniques and hierarchical aggregation functions decision trees for the classification of epilepsy risk levels from EEG signals

The purpose of this paper is to identify the practicability of hierarchical soft (max-min) decision trees in optimization of fuzzy outputs in 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 classifier with max-min criteria) four types are applied on the classified data to identify the optimized risk level (singleton) which characterizes the patientpsilas 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 95.88 % was obtained at QVpsilas of 22.43 in the hierarchical decision tree optimization when compared to the value of 40% and 6.25 through fuzzy classifier respectively. It is identified the hierarchical soft decision tree (Hier & h min-max) method is a good post classifier.