• Buajieerguli Maimaiti, Hongmei Meng, Yudan Lv, Jiqing Qiu, Zhanpeng Zhu, Yinyin Xie, Yue Li, Yu-ChengDepartment of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China., Weixuan Zhao, Jiayu Liu, and Mingyang Li.
• Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China.
• Neuroscience. 2022 Jan 15; 481: 197-218.

AbstractThe unpredictability of epileptic seizures is one of the most problematic aspects of the field of epilepsy. Methods or devices capable of detecting seizures minutes before they occur may help prevent injury or even death and significantly improve the quality of life. Machine learning (ML) is an emerging technology that can markedly enhance algorithm performance by interpreting data. ML has gained increasing attention from medical researchers in recent years. Its epilepsy applications range from the localization of the epileptic region, predicting the medical or surgical outcome of epilepsy, and automated electroencephalography (EEG) analysis to seizure prediction. While ML has good prospects with regard to detecting epileptic seizures via EEG signals, many clinicians are still unfamiliar with this field. This work briefly summarizes the history and recent significant progress made in this field and clarifies the essential components of the automatic seizure detection system using ML methodologies for clinicians. This review also proposes how neurologists can actively contribute to ensure improvements in seizure prediction using EEG-based ML.Copyright © 2021 IBRO. Published by Elsevier Ltd. All rights reserved.

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