Objective: Video-electroencephalography (vEEG) is an important component of epilepsy diagnosis and management. Nevertheless, inpatient vEEG monitoring fails to capture seizures in up to one third of patients during diagnostic and pre-surgical monitoring. We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG and improve diagnostic yield. Methods: We used a database of ambulatory vEEG studies to select a cohort with linked electronic seizure diaries of more than 20 reported seizures over at least 8 weeks. The total cohort included 48 participants. Diary seizure times were used to detect individuals' multi-day cycles and estimate times of high seizure risk. We then compared whether estimated seizure risk was significantly different between diagnostic and non-diagnostic vEEGs, and between vEEG with and without recorded epileptic activity. Results: Estimated seizure risk was significantly higher for diagnostic vEEGs and vEEGs with epileptic activity. Across all cycle strengths, the average time in high risk during vEEG was 29.1% compared with 14% for the diagnostic/non-diagnostic groups and 32% compared to 18% for the epileptic activity/no epileptic activity groups. On average, 62.5% of the cohort showed increased time in high risk during vEEG when epileptic activity was recorded (compared to 28% of the cohort where epileptic activity was not recorded). For diagnostic vEEGs, 50% of the cohort had increased time in high risk, compared to 21.5% for non-diagnostic vEEGs. Significance: This study provides a proof of principle that scheduling monitoring times based on personalized seizure risk forecasts can improve the yield of vEEG. Importantly, forecasts can be developed at low cost from mobile seizure diaries. A simple scheduling tool to improve diagnostic outcomes has the potential to reduce the significant cost and risks associated with delayed or missed diagnosis in epilepsy.

中文翻译：

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自我报告的癫痫发作可能性的周期对应于诊断性癫痫监测的产量

目的：视频脑电图（vEEG）是癫痫诊断和管理的重要组成部分。尽管如此，在诊断和术前监测期间，住院vEEG监测未能捕获多达三分之一患者的癫痫发作。我们假设个性化的癫痫发作预测可用于优化vEEG的时机并提高诊断率。方法：我们使用动态vEEG研究的数据库来选择一个队列，该队列的电子癫痫日记至少在8周内有20多个报告的癫痫发作。整个队列包括48名参与者。日记癫痫发作时间用于检测个人的多日周期并估算高癫痫发作风险的时间。然后，我们比较了诊断性和非诊断性vEEG之间估计的癫痫发作风险是否存在显着差异，在有和没有记录的癫痫活动的vEEG之间。结果：诊断性vEEG和具有癫痫活动的vEEG的估计癫痫发作风险明显更高。在所有周期强度中，vEEG期间处于高风险的平均时间为29.1％，诊断/非诊断组为14％，癫痫活动/无癫痫活动组为32％，而平均为18％。平均而言，当记录到癫痫活动时，有62.5％的队列显示出在vEEG期间处于高风险时期的时间增加（相比之下，没有记录到癫痫活动的队列发生率为28％）。对于诊断性vEEG，高危人群的时间增加了50％，而非诊断性vEEGs则为21.5％。意义：这项研究提供了原理证明，即基于个性化的癫痫发作风险预测安排监视时间可以提高vEEG的产生。重要的是，可以通过移动缉获日记以低成本开发预报。一种简单的调度工具可以改善诊断结果，有可能降低与癫痫病延迟诊断或漏诊相关的重大成本和风险。