Classification and Analysis of EEG Brain Signals for Finding Epilepsy Risk LevelsUsing SVM

Electroencephalogram (EEG)remainsthe brain signal processing system that tolerates gaining the appreciative of the multipartinternal mechanisms of the brain and irregular brain waves have exposed to be associatedthroughexact brain syndromes. The study of brain waves shows an essentialpart in analysis of dissimilar brain syndromes.Currently there are many people in the world who are suffering from severe brain related illnesses. The physical state or condition of the patient can be assessed by analysing his EEG data. Doctors thus feel a need to check on the EEG data of a patient from time to time. This is where the proposed system comes into play. It provides a means for doctors to analyse the patient's EEG data without direct interaction. Objective of this research work is to associate the classification of epileptic risk level from (Electroencephalogram) EEG signal thenperformance analysis of Support Vector Machine (SVM) and Minimum Relative Entropy (MRE) in optimization of fuzzy crops in the classification of epileptic risk levels from EEG warning signal. The fuzzy preclassifier is castoff to classify the risk phases of epilepsy based on extractedlimitssimilarto energy, variance, peaks, sharp, spike waves, duration, events and covariance after the EEG signs of the patient. Support Vector Machine and Minimum Relative Entropy are useful on the categorized data to recognize the enhanced risk level (singleton) which designates the patient's risk level. The effectiveness of the aboveapproaches is related based on the bench mark boundaries such as Performance Index (PI), then Quality Value (QV).