Performance analysis of fuzzy techniques hierarchical aggregation functions decision trees and Support Vector Machine (SVM)for the classification of epilepsy risk levels from EEG signals

Abstract

The objective of this paper is to compare the performance of Hierarchical Soft (max-min) Decision Trees and Support Vector Machine (SVM) 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 (HDT post classifiers with max-min criteria of four types) and Support Vector Machine (SVM) 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).