Department of Neurology and Neuroscience Center, First Hospital of Jilin University, Changchun, Jilin, People's Republic of China.
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. doi: 10.1016/j.neuroscience.2021.11.017. Epub 2021 Nov 16.
The 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.
癫痫发作的不可预测性是癫痫领域最具问题性的方面之一。能够在癫痫发作前几分钟检测到发作的方法或设备可能有助于预防伤害甚至死亡，并显著提高生活质量。机器学习 (ML) 是一种新兴技术，通过解释数据可以显著提高算法性能。近年来，ML 引起了医学研究人员的越来越多的关注。其在癫痫中的应用范围从癫痫区域的定位、预测癫痫的医疗或手术结果，到自动脑电图 (EEG) 分析和癫痫发作预测。虽然 ML 有很好的前景可以通过 EEG 信号检测癫痫发作，但许多临床医生对这一领域仍然不熟悉。这项工作简要总结了该领域的历史和最近的重大进展，并为临床医生使用 ML 方法阐明了基于 EEG 的自动癫痫发作检测系统的基本组成部分。本综述还提出了神经病学家如何积极参与使用基于 EEG 的 ML 来确保癫痫发作预测的改进。
