Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, Vic., Australia.
Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, Vic., Australia.
Epilepsia. 2018 May;59(5):1027-1036. doi: 10.1111/epi.14065. Epub 2018 Apr 6.
We report on patient-specific durations of postictal periods in long-term intracranial electroencephalography (iEEG) recordings. The objective was to investigate the relationship between seizure duration and postictal suppression duration.
Long-term recording iEEG from 9 patients (>50 seizures recorded) were analyzed. In total, 2310 seizures were recorded during a total of 13.8 years of recording. Postictal suppression duration was calculated as the duration after seizure termination until total signal energy returned to background levels. The relationship between seizure duration and postictal suppression duration was quantified using the correlation coefficient (r). The effects of populations of seizures within patients, on correlations, were also considered. Populations of seizures within patients were distinguished by seizure duration thresholds and k-means clustering along the dimensions of seizure duration and postictal suppression duration. The effects of bursts of seizures were also considered by defining populations based on interseizure interval (ISI).
Seizure duration accounted for 40% of postictal suppression duration variance, aggregated across all patients and seizures. Seizure duration accounted for more than 25% of the variance in postictal suppression duration in 2 patients and accounted for less than 25% in the remaining 7. In 3 patients, heat maps showed multiple distinct postictal patterns indicating multiple populations of seizures. When accounting for these populations, seizure duration accounted for less than 25% of the variance in postictal duration in all populations. Variance in postictal suppression duration accounted for less than 10% of ISI variance in all patients.
We have previously demonstrated that some patients have multiple seizure populations distinguishable by seizure duration. This article shows that different seizure populations have distinct and consistent postictal behaviors. The existence of multiple populations in some patients has implications for seizure management and forecasting, whereas the distinct postictal behaviors may have implications for sudden unexpected death in epilepsy (SUDEP) prediction and prevention.
我们报告长程颅内脑电图（iEEG）记录中癫痫发作后时期的患者特异性持续时间。目的是研究癫痫发作持续时间与癫痫发作后抑制持续时间之间的关系。
分析了 9 名（记录超过 50 次癫痫发作）患者的长程记录 iEEG。共记录了 2310 次癫痫发作，总记录时间为 13.8 年。癫痫发作后抑制持续时间的计算方法是从癫痫发作终止到总信号能量恢复到背景水平的时间。使用相关系数（r）量化癫痫发作持续时间与癫痫发作后抑制持续时间之间的关系。还考虑了患者内癫痫发作群体对相关性的影响。通过使用基于癫痫发作持续时间和癫痫发作后抑制持续时间维度的阈值和 k-均值聚类，区分了患者内的癫痫发作群体。还通过定义基于发作间间隔（ISI）的群体来考虑癫痫发作爆发的影响。
癫痫发作持续时间解释了所有患者和癫痫发作的癫痫发作后抑制持续时间变异性的 40%。在 2 名患者中，癫痫发作持续时间解释了癫痫发作后抑制持续时间变异性的 25%以上，而在其余 7 名患者中解释了不到 25%。在 3 名患者中，热图显示了多个不同的癫痫发作后模式，表明存在多个癫痫发作群体。在考虑到这些群体时，在所有群体中，癫痫发作持续时间解释了癫痫发作后持续时间变异性的不到 25%。在所有患者中，癫痫发作后抑制持续时间变异性解释了 ISI 变异性的不到 10%。
我们之前已经证明，一些患者存在可通过癫痫发作持续时间区分的多个癫痫发作群体。本文表明，不同的癫痫发作群体具有不同且一致的癫痫发作后行为。在一些患者中存在多个群体对癫痫发作管理和预测具有影响，而不同的癫痫发作后行为可能对癫痫猝死（SUDEP）预测和预防具有影响。
