Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.
Technical University Munich, Munich, Germany.
Epilepsia. 2023 Apr;64(4):1074-1086. doi: 10.1111/epi.17525. Epub 2023 Feb 17.
Understanding fluctuations in seizure severity within individuals is important for determining treatment outcomes and responses to therapy, as well as assessing novel treatments for epilepsy. Current methods for grading seizure severity rely on qualitative interpretations from patients and clinicians. Quantitative measures of seizure severity would complement existing approaches to electroencephalographic (EEG) monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative EEG markers that assess the spread and intensity of abnormal electrical activity during and after seizures.
We analyzed intracranial EEG (iEEG) recordings of 1009 seizures from 63 patients. For each seizure, we computed 16 markers of seizure severity that capture the signal magnitude, spread, duration, and postictal suppression of seizures.
Quantitative EEG markers of seizure severity distinguished focal versus subclinical seizures across patients. In individual patients, 53% had a moderate to large difference (rank sum , ) between focal and subclinical seizures in three or more markers. Circadian and longer term changes in severity were found for the majority of patients.
We demonstrate the feasibility of using quantitative iEEG markers to measure seizure severity. Our quantitative markers distinguish between seizure types and are therefore sensitive to established qualitative differences in seizure severity. Our results also suggest that seizure severity is modulated over different timescales. We envisage that our proposed seizure severity library will be expanded and updated in collaboration with the epilepsy research community to include more measures and modalities.
了解个体内部癫痫发作严重程度的波动对于确定治疗结果和治疗反应,以及评估癫痫的新疗法非常重要。目前,癫痫发作严重程度的分级方法依赖于患者和临床医生的定性解释。癫痫发作严重程度的定量测量方法将补充现有的脑电图(EEG)监测、结果监测和癫痫发作预测方法。因此,我们开发了一个定量 EEG 标记库,用于评估癫痫发作期间和之后异常电活动的传播和强度。
我们分析了 63 名患者的 1009 次颅内 EEG(iEEG)记录。对于每一次癫痫发作,我们计算了 16 种癫痫发作严重程度的标记物,这些标记物捕捉了信号幅度、传播、持续时间和癫痫发作后的抑制。
癫痫发作严重程度的定量 EEG 标记物能够区分患者的局灶性与亚临床性癫痫发作。在个别患者中,53%的患者在三种或更多标记物中存在局灶性和亚临床性癫痫发作之间的中度至较大差异(秩和检验, )。大多数患者的严重程度存在昼夜节律和较长时间的变化。
我们证明了使用定量 iEEG 标记物来测量癫痫发作严重程度的可行性。我们的定量标记物可以区分癫痫发作类型,因此对已建立的癫痫发作严重程度的定性差异敏感。我们的结果还表明,癫痫发作严重程度在不同时间尺度上受到调节。我们设想,我们提出的癫痫发作严重程度库将与癫痫研究社区合作进行扩展和更新,包括更多的措施和模态。