Classification and Analysis of Epileptic Seizure

Abstract

Epilepsy is a neurological disease where nearly fifty million people are affected all around the world. EEG plays a critical role in monitoring the brain activity of patients with epilepsy and also plays a significant role in diagnosing Epilepsy. Epileptic seizures are life-threatening since it causes severe damage to the brain of the patient. There are five different kinds of frequency bands in EEG signals. Features extraction plays a significant role in the effectiveness of EEG-based Epileptic seizure detection. The analysis involves using prominent features which are extracted from the signals. Classification is done using machine learning techniques, among various machine learning algorithms Nonlinear SVMs are found to have the highest accuracy of 96.25% when compared to that linear SVM.