EEG seizure detection: concepts, techniques, challenges, and future trends

• Published in: Multimedia tools and applications Vol. 82; no. 27; pp. 42021 - 42051
• Main Authors: Ein Shoka, Athar A., Dessouky, Mohamed M., El-Sayed, Ayman, Hemdan, Ezz El-Din
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2023; Springer Nature B.V
• Subjects: Brain; Central nervous system; Computer Communication Networks; Computer Science; Convulsions & seizures; Data Structures and Information Theory; Electroencephalography; Epilepsy; Machine learning; Multimedia Information Systems; Patients; Remote monitoring; Seizures; Special Purpose and Application-Based Systems; Electroencephalography (EEG); Features extraction; Artificial intelligence; Epilepsy; Classification; IoT
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-15052-2

A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and time-consuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart healthcare systems. Finally, challenges and open research points in EEG seizure detection are investigated.