Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Department of Neurology, Yale University, New Haven, Connecticut, USA.
Epilepsia. 2023 Dec;64 Suppl 3(Suppl 3):S62-S71. doi: 10.1111/epi.17546. Epub 2023 Mar 8.
A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.
最近,在癫痫预测的漫长曲折道路上取得了很多进展。在这里,我们简要回顾一下在瑞士伯尔尼大学举行的国际癫痫发作技术与分析会议(ICTALS 2022)上讨论的一些里程碑式进展。最近,可穿戴和植入式设备的发展为癫痫预测带来了主要动力,这些设备不仅可以记录脑电图,还可以记录运动行为、声音信号和自主神经系统的各种信号等数据。这种多模态监测可以进行超长时间的监测,时间跨度可以覆盖数月或数年。因此,从这些数据中提取的特征和指标现在可以更完整地评估癫痫发作的动态。最值得注意的是,这证实了人们长期以来怀疑的发作间期癫痫样活动、癫痫发作风险和癫痫发作的周期性特征。这些时间尺度包括日常、多天和每年的周期。网络科学领域的交叉学科方法也为进展提供了动力。将癫痫视为一种大规模网络障碍,可以为不断演变的癫痫大脑的发作前动力学提供新的视角。除了在特定预测时间内发生癫痫的离散预测外,该领域还将预测范围扩大到了时间上不断演变的癫痫发作风险的概率预测。这一转变促使人们引入了更多的指标来量化预测算法的性能,这些指标应与受约束的随机零模型的机会性能进行比较。目前一个极其重要的任务是找到最佳方式将癫痫预测算法的输出传达给患者、护理人员和临床医生,以便产生社会效益并改善患者的生活质量。