Forecasting cycles of seizure likelihood
Forecasting cycles of seizure likelihood
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DOI：
10.1111/epi.16485
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发表时间：
2020-03-27
期刊：
影响因子：
5.6
通讯作者：
Freestone, Dean R.
中科院分区：
文献类型：
--
作者：
Karoly, Philippa J.;Cook, Mark J.;Freestone, Dean R.
目的癫痫发作的不可预测性被认为是癫痫患者生活中最具挑战性的方面之一。癫痫发作的可能性可能受到一系列环境和生理因素的影响，这些因素很难衡量和量化。然而，一些概括性的模式已经在癫痫发作中被证明。大多数癫痫患者在发作期间表现出昼夜节律，许多人也表现出较慢的、多天的模式。发作周期可以使用一系列记录方式来测量，包括自我报告的电子发作日记。本研究旨在开发基于移动发作日记应用程序的个性化预测。方法使用来自50个应用程序用户(平均每个受试者109次发作)的数据，对基于昼夜和多天发作周期的预测进行伪前瞻性测试。根据个人报告的癫痫发作时间估计出个体的最强周期，并用来推断未来癫痫发作的可能性。使用NeuroVista数据集的自我报告事件和脑电发作对预测方法进行了验证。NeuroVista数据集是一个现有的长期脑电数据库，已被广泛用于开发预测算法。结果验证数据集显示，基于自我报告周期的癫痫可能性预测可以预测大约一半的队列的脑电发作。仅使用手机应用程序日记进行预测，用户平均有67.1%的时间处于低风险状态，14.8%的时间处于高风险警告状态。平均而言，69.1%的癫痫发作发生在高风险状态，10.5%的癫痫发作发生在低风险状态。重大癫痫发作日记应用程序可以提供准确的个性化癫痫发作可能性预测，并与脑电发作具有临床相关性。这些结果立即有可能转化为使用移动日记应用程序进行的前瞻性癫痫发作预测试验。我们希望癫痫发作预测应用程序有一天能给癫痫患者更大的信心来管理他们的日常活动。
Objective Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app.Methods Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms.Results The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low-risk state, with 14.8% of their time in a high-risk warning state. On average, 69.1% of seizures occurred during high-risk states and 10.5% of seizures occurred in low-risk states.Significance Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.