Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges

Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians' ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the 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 MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, 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 that can further help researchers predict epileptic seizures.

Automated summary

Epilepsy is a life-threatening neurological disorder characterized by recurrent unprovoked seizures due to abnormal chemical changes in the brain. Studies have focused on using machine learning methods for automatic diagnosis and early prediction of epileptic seizures. These studies primarily involve feature extraction from EEG data and classification using machine learning classifiers. This study provides a systematic literature review of feature selection methods and classification performance, identifying commonly used techniques and classifiers for accurate classification. The research also examines benchmark datasets, analyzes the performance of classifiers, and highlights gaps and opportunities for further research in predicting epileptic seizures.