Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia.
Department of Medicine, University of Melbourne, Fitzroy, Victoria, Australia.
Eur J Neurol. 2022 Feb;29(2):375-381. doi: 10.1111/ene.15166. Epub 2021 Nov 15.
Epilepsy is characterized by recurrent seizures that have a variety of manifestations. The severity of, and risks for patients associated with, seizures are largely linked to the duration of seizures. Methods that determine seizure duration based on seizure onsets could be used to help mitigate the risks associated with what might be extended seizures by guiding timely interventions.
Using long-term intracranial electroencephalography (iEEG) recordings, this article presents a method for predicting whether a seizure is going to be long or short by analyzing the seizure onset. The definition of long and short depends on each patient's seizure distribution. By analyzing 2954 seizures from 10 patients, patient-specific classifiers were built to predict seizure duration given the first few seconds from the onset.
The proposed methodology achieved an average area under the receiver operating characteristic curve (AUC) performance of 0.7 for the 5 of 10 patients with above chance prediction performance (p value from 0.04 to 10 ).
Our results imply that the duration of seizures can be predicted from the onset in some patients. This could form the basis of methods for predicting status epilepticus or optimizing the amount of electrical stimulation delivered by seizure control devices.
癫痫的特征是反复发作,具有多种表现。发作的严重程度和患者的风险与发作持续时间密切相关。基于发作起始来确定发作持续时间的方法可以用于通过指导及时干预来帮助减轻可能延长的发作相关风险。
本文使用长期颅内脑电图 (iEEG) 记录,通过分析发作起始,提出了一种预测发作是长还是短的方法。长和短的定义取决于每个患者的发作分布。通过分析 10 名患者的 2954 次发作,针对每个患者构建了特定于患者的分类器,以在发作起始后的前几秒钟预测发作持续时间。
对于 5 名预测性能高于机会水平的患者中的 10 名患者,所提出的方法实现了平均接收者操作特征曲线 (AUC) 性能为 0.7(p 值从 0.04 到 10)。
我们的结果表明,在某些患者中可以从发作起始预测发作持续时间。这可以为预测癫痫持续状态或优化发作控制设备所提供的电刺激量的方法提供基础。