Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia.
Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia.
Epilepsia. 2021 Feb;62 Suppl 1:S2-S14. doi: 10.1111/epi.16541. Epub 2020 Jul 26.
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day-to-day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure-forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure-forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.
癫痫是一种独特的神经系统疾病，其特征是反复发作，其病因、潜在生物标志物、触发因素和模式在个体之间有所不同。癫痫发作的不可预测性会增加癫痫患者的恐惧和焦虑，使他们难以参与日常活动。几十年来，癫痫研究人员一直优先开发癫痫预测算法以对抗发作性癫痫，但预测算法在临床环境中的实用性和有效性尚未得到充分研究。相比之下，癫痫预测理论上提供了任何时间发生癫痫的概率（而不是预测下一次癫痫发作的发生），可能更具可行性。在过去的十年中，癫痫预测领域取得了许多进展，包括由于机器学习而改进的算法，以及探索非 EEG 癫痫易感性测量方法，如生理生物标志物、行为变化、环境驱动因素和周期性癫痫模式。例如，最近研究个体癫痫模式周期性的工作确定，超过 90%的人癫痫发作存在昼夜节律，许多人还经历多日、每周或更长时间的周期。癫痫易感性的其他潜在指标包括压力水平、心率和睡眠质量，所有这些都有可能在长时间内进行非侵入性监测。癫痫预测设备有许多可能的应用，包括改善癫痫患者的生活质量、指导治疗计划和药物滴定、优化术前监测以及聚焦科学研究。为了实现这一潜力，了解癫痫预测设备的用户需求、继续推进预测算法以及设计癫痫预测前瞻性临床试验的明确指南至关重要。
