Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast.
Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast.
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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。
DOI：
10.1111/epi.16785
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发表时间：
2021-03
期刊：
影响因子：
5.6
通讯作者：
Freestone DR
中科院分区：
文献类型：
--
作者：
Payne DE;Dell KL;Karoly PJ;Kremen V;Gerla V;Kuhlmann L;Worrell GA;Cook MJ;Grayden DB;Freestone DR
大多数癫痫发作预测算法依赖于脑电图记录的特定特征。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的发生，但尚未充分探索作为癫痫发作预测的先验信息，在患者特定的分析。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。本研究对8例患者的数据进行了事后分析，这些患者共有12.2年的连续颅内脑电图记录（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。患者也有睡眠评分和特定地点的天气数据。为每个特征生成未来癫痫发作可能性的直方图。使用贝叶斯方法将不同特征联合收割机组合到癫痫发作可能性的总体预测中来测量个体特征的预测效用。使用受试者工作曲线下面积比较不同特征组合的性能。性能评价为伪前瞻性。对于所研究的八名患者，可以使用睡眠（五名患者），天气（两名患者）和时间特征（六名患者）预测癫痫发作的概率准确性。在6名患者中，使用组合特征的预测效果明显优于随机预测。对于其中四名患者，组合预测优于任何单个特征。环境和生理数据，包括睡眠、天气和时间特征，提供了关于即将发生的癫痫发作的重要预测信息。虽然预测的效果不如使用侵入性颅内脑电图的算法，但结果明显高于偶然性。从个体的历史发作记录导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非侵入性地测量。
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.
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影响因子：
2.4
作者：
ELLINGSON, RJ;WILKEN, K;BENNETT, DR
通讯作者：
BENNETT, DR
影响因子：
5.6
作者：
Doherty, Michael J.;Youn, Clover;Haltiner, Alan M.
通讯作者：
Haltiner, Alan M.
影响因子：
3.2
作者：
Kerem, DH;Geva, AB
通讯作者：
Geva, AB
DOI：
10.1093/brain/awv073
发表时间：
2015-06
期刊：
Brain : a journal of neurology
影响因子：
--
作者：
Frauscher B;von Ellenrieder N;Ferrari-Marinho T;Avoli M;Dubeau F;Gotman J
通讯作者：
Gotman J
影响因子：
2.4
作者：
Foldvary-Schaefer, N;Grigg-Damberger, M
通讯作者：
Grigg-Damberger, M