A Comparative Assessment of Machine Learning Techniques for Epilepsy Detection using EEG Signal

Epilepsy is a psychological issue that causes ridiculous, repetitive seizures. A seizure is an unexpected surge of electrical activity in the cerebrum. Since traditional time or recurrence, area examination is discovered insufficient to portray the qualities of non-fixed signals, for example, electroencephalography (EEG) signal. In this paper, we propose to change the EEG information utilizing Detrended Fluctuation Analysis, Singular Value Decomposition Entropy, Higuchi Fractal Dimension, and Petrosian Fractal Dimension works and plan vectors. These vectors are utilized as highlights for ten distinctive Machine Learning classifiers: Logistic Regression, Support Vector Classifier, Linear Support Vector Classifier, K Nearest Neighbors Classifier, Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier, Stochastic Gradient Descent Classifier, Multilayer Perceptron Classifier, Gaussian Process Classifier, to group epileptic seizure starting from various parts and condition of the cerebrum. Our outcomes show that the proposed strategy gives better exactness, accuracy, and review in contrast with the current strategies for multiclass Epileptic Seizure Classification.