OBJECTIVE Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. METHODS This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0-2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location-specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. RESULTS For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature. SIGNIFICANCE Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

中文翻译：

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识别癫痫风险因素：使用贝叶斯预测比较睡眠、天气和时间特征

目标 大多数癫痫发作预测算法都依赖于脑电图记录特有的特征。长期以来，人们一直怀疑环境和生理因素（例如天气和睡眠）会影响大脑活动和癫痫发作，但尚未充分探索作为特定患者分析中癫痫发作预测的先验信息。该研究旨在量化睡眠、天气和时间因素（一天中的时间、一周中的一天和月相）是否可以提供可用于改善癫痫发作预测的预测先验概率。方法 本研究对 8 名患者的数据进行事后分析，这些患者的颅内脑电图连续记录总计 12.2 年（平均 = 1.5 年，范围 = 1.0-2.1 年），最初是在一项前瞻性试验中收集的。患者还拥有睡眠评分和特定位置的天气数据。为每个特征生成未来癫痫发作可能性的直方图。使用贝叶斯方法测量单个特征的预测效用，将不同的特征组合成癫痫发作可能性的整体预测。使用接收器操作曲线下面积比较不同特征组合的性能。绩效评估是伪前瞻性的。结果 对于所研究的 8 名患者，使用睡眠（5 名患者）、天气（2 名患者）和时间特征（6 名患者）可以预测癫痫发作高于机会准确性。在 6 名患者中，使用组合特征进行预测的效果明显好于偶然。对于其中四名患者，综合预测优于任何单个特征。意义 环境和生理数据，包括睡眠、天气和时间特征，为即将发生的癫痫发作提供重要的预测信息。尽管预测的表现不如使用侵入性颅内脑电图的算法好，但结果明显高于偶然。从个人历史癫痫发作记录中得出的互补信号特征可以提供有用的先验信息，以增强传统的癫痫发作检测或预测算法。重要的是，本研究中使用的许多预测特征可以无创测量。结果明显高于偶然。从个人历史癫痫发作记录中得出的互补信号特征可以提供有用的先验信息，以增强传统的癫痫发作检测或预测算法。重要的是，本研究中使用的许多预测特征可以无创测量。结果明显高于偶然。从个人历史癫痫发作记录中得出的互补信号特征可以提供有用的先验信息，以增强传统的癫痫发作检测或预测算法。重要的是，本研究中使用的许多预测特征可以无创测量。