Performance Comparison of Machine Learning Techniques for Epilepsy Classification and Detection in EEG Signal

Abstract

Epilepsy is a neurological affliction that in impact around 1% of humankind. Around 10% of the United States populace involvement with minimum a solitary convulsion in their activity. Epilepsy has recognized respectively tendency of the cerebrum outcomes unforeseen blasts of weird electrical action which disturbs the typical working of the mind. Since spasms by and large happen once in a while and are unforeseeable, seizure identification frameworks are proposed for seizure discovery amid long haul electroencephalography (EEG). In this exploration, we utilize DWT for highlight extraction and do correlation for all kind of Machine learning order like SVM, Nearest Neighbor Classifiers, Logistic relapse, Ensemble classifiers and so on. In this examination classification accuracy of Fine Gaussian SVM recorded as 100% and it has better as compare to other existing machine learning approaches.