Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.
Epilepsia. 2021 Feb;62(2):371-382. doi: 10.1111/epi.16785. Epub 2020 Dec 30.
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.
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.
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.
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.
大多数癫痫发作预测算法都依赖于特定于脑电图记录的特征。环境和生理因素，如天气和睡眠，长期以来一直被怀疑会影响大脑活动和癫痫发作，但在针对特定患者的分析中，尚未充分探索这些因素作为癫痫发作预测的先验信息。本研究旨在量化睡眠、天气和时间因素（一天中的时间、一周中的天数和月相）是否可以提供预测性先验概率，这些概率可能用于改善癫痫发作预测。
本研究对来自 8 名患者的 12.2 年连续颅内脑电图记录（平均=1.5 年，范围=1.0-2.1 年）进行了事后分析，这些数据最初是在一项前瞻性试验中收集的。患者还进行了睡眠评分和位置特定的天气数据记录。为每个特征生成未来癫痫发作可能性的直方图。使用贝叶斯方法将不同特征组合成癫痫发作可能性的总体预测，来衡量单个特征的预测效用。使用接收器操作曲线下的面积比较不同特征组合的性能。性能评估是伪前瞻性的。
在研究的 8 名患者中，使用睡眠（5 名患者）、天气（2 名患者）和时间特征（6 名患者）可以在一定程度上预测癫痫发作。使用组合特征的预测明显优于机会在 6 名患者中。对于其中的 4 名患者，组合预测的效果优于任何单个特征。
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了重要的预测信息。尽管预测结果不如使用侵入性颅内脑电图的算法好，但结果明显高于机会水平。从个体的历史癫痫发作记录中得出的补充信号特征可能为增强传统的癫痫发作检测或预测算法提供有用的先验信息。重要的是，本研究中使用的许多预测特征可以进行非侵入性测量。
