Kerr Wesley T, McFarlane Katherine N, Figueiredo Pucci Gabriela
Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
Front Neurol. 2024 Jul 11;15:1425490. doi: 10.3389/fneur.2024.1425490. eCollection 2024.
Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.
癫痫发作对生活质量和死亡率有深远影响，部分原因在于其检测和预测都具有挑战性。癫痫发作的检测依赖于准确区分由异常癫痫样活动引起的短暂神经症状与由不同原因导致的类似症状。癫痫发作预测旨在确定个体癫痫发作可能性的高低，这与癫痫发作的预测相关。机器学习和人工智能是与神经诊断监测技术相结合的数据驱动技术，试图完成这两项任务。在这篇叙述性综述中，我们描述了现有的癫痫发作检测和预测的软件及硬件方法，以及如何评估新技术在临床实践中未来应用性能的概念。这些技术包括有脑电图（EEG）和无脑电图的长期监测，报告显示其具有非常高的灵敏度以及减少了误报检测。此外，我们描述了癫痫发作检测和预测对临床试验中癫痫新疗法评估的影响。基于这些现有数据，利用机器学习和人工智能进行长期癫痫发作检测和预测可能会从根本上改变癫痫患者的临床护理，但相对于当前标准，需要多个验证步骤来严格证明其益处和成本。
