Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML be used classify signals employing feature extraction techniques. This work focuses automated detection for Various algorithms are investigated, such as Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), neural network(ANN), Naïve Bayes, Random Forest (RF) distinguish injected from normal ones high accuracy. In work, 54 Discrete wavelet transforms (DWTs) extraction, the similarity distance applied identify most powerful features. The features then selected form matrix. matrix subsequently train ML. approach evaluated through different metrics F-measure, precision, accuracy, Recall. experimental results show that SVM Bagging classifiers some data set combinations, outperforming all other