Abstract

This study aims to improve the automatic detection of epileptic seizures (ESs) using machine learning (ML) algorithms applied to electroencephalography (EEG) brainwave data. Previous studies based on a database published online showed high seizure detection accuracies, but they contrasted seizure activity to all kinds of non-seizure EEG activity, recorded from different populations (healthy and patient) and different types of electrodes (surface and intracranial). Here we decided to focus on detecting seizures from non-seizure activity recorded from the same type of electrodes in the same group of patients. We applied different ML classifiers such as Extreme Gradient Boosting (XgBOOST), Naive Bayes (NB), k-Nearest Neighbor (k-NN), Random Forest (RF), Support Vector Machine (SVM), Linear Regression (LR), and Decision Tree (DT). The best Area Under Curve (AUC) value is given by XgBOOST with 95.84%. This research helps to improve the detection of human ES in EEG signal recordings.

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