A review on Epileptic Seizure Detection using Machine Learning

TL;DR

This paper systematically reviews machine learning techniques for epileptic seizure detection, focusing on feature extraction, classifier performance, datasets, and identifying research gaps for future advancements.

Contribution

It provides a comprehensive taxonomy of current methods, analyzes dataset characteristics, and highlights challenges and opportunities in seizure prediction research.

Findings

Most used feature extraction methods identified

Common classifiers include SVM and neural networks

Gaps in dataset diversity and model robustness highlighted

Abstract

Epilepsy is a neurological brain disorder which life threatening and gives rise to recurrent seizures that are unprovoked. It occurs due to the abnormal chemical changes in our brain. Over the course of many years, studies have been conducted to support automatic diagnosis of epileptic seizures for the ease of clinicians. For that, several studies entail the use of machine learning methods for the early prediction of epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine and then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of feature selection process as well as the classification performance. This study was limited to the finding of most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MPDI, IEEEXplore, Wiley, Elsevier, ACM, Springerlink and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges and opportunities which can further help researchers in prediction of epileptic seizure