Comparison of Different Machine Learning Algorithms to Classify Epilepsy Seizure from EEG Signals †

Abstract

Recurrent seizures are a symptom of a central nervous system disease called epilepsy. The duration of these seizures lasts less than a few seconds or sometimes minutes. There are very few ways to record seizures, and one of them is EEG. EEG systems mainly consist of scalp electrodes that record electrical activity. These EEG data are often complex signals containing noise and artifacts. Accurate classification of epileptic seizures is a major challenge, as manual seizure identification is a laborious and challenging endeavor for neurologists. An automated method for seizure detection and categorization was required to address this issue. In this paper, we used machine learning and proposed a model that predicts the behavior of these signals and classifies seizures. The Epileptic Seizure Recognition Data Set from the UCI Machine Learning Repository was the dataset used in this work. The model is evaluated on various models such as XGboost, Extra Tree Classifier, Random Forest, etc. Using measures like F1 score, recall, and precision, the proposed approaches have been assessed. The results indicate that Random Forest produced the superior result of 0.943 F1 score, and XGB achieved a slightly lower F1 score of 0.933. Moreover, Random Forest has the highest accuracy of 0.977.