A Structured Soft (Max-Min) Decision Trees for Patient Specific Fuzzy Classifier in the Classification of Epilepsy Risk Levels from EEG Signals
The objective of this research is to investigate the feasibility of structured Soft (max-min) decision trees 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. Soft decision tree (post classifier with max-min criteria) six types are applied on the classified data to identify the optimized risk level (singleton) which 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), sensitivity, specificity and quality value (QV).