Enhancing Epileptic Seizure Prediction with Machine Learning and EEG Analysis

Abstract

Prediction of epileptic seizures in accurate manner and on time prediction can help in improving the lifestyle of the affected people. Many computational intelligence methods have been developed for EEG signal analysis. Since they can only handle the algorithm's complexity, new strategies have been developed to obtain the desired outcome. The goal of this work is to create an innovative method that provides the highest classification performance with the least computational expenses. This work concentrates on analyzing various deep learning models and machine learning classifiers like decision tree (C4.5), Naïve Bayes (NB), Support Vector Machine (SVM), logistic regression (LR), k-nearest neighbour (k-NN) and adaboosting model. By considering the results obtained from various classifiers, it is noted that C4.5 works well compared to other approaches. By examining the results obtained from various classifiers, this research provides valuable insights into the ensemble machine learning approaches for enhancing the accuracy and efficiency of epileptic seizure prediction from EEG signals.