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Abstract：
Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments.
Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC).
Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures.
Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.
Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC).
Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures.
Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.
摘要：
目的：先前的研究表明，癫痫患者可能能够预测自己的癫痫发作。这项研究旨在评估先兆症状之间的关系，感知的癫痫发作风险，以及未来和最近自我报告和脑电图确认的癫痫患者在其自然家庭环境中的癫痫发作。
方法：收集有和没有并发脑电图记录的患者的长期电子调查。从电子调查中获得的信息包括药物依从性，睡眠质量,心情,压力，调查前感知的癫痫发作风险和癫痫发作发生情况。确定了EEG癫痫发作。使用单变量和多变量广义线性混合效应回归模型来估计比值比（OR）以评估关系。使用将OR转换为曲线下等效面积（AUC）的数学公式将结果与癫痫发作预测分类器和设备预测文献进行比较。
结果：54名受试者返回了10,269个电子调查条目，四名受试者同时采集脑电图记录。单因素分析显示应激增加(OR=2.01，95%CI=[1.12，3.61]，AUC=0.61，p=0.02）与未来自我报告癫痫发作的相对几率增加相关。多变量分析表明，以前自我报告的癫痫发作（5.37，[3.53，8.16]，0.76，<0.001）与未来自我报告的癫痫发作和高感知的癫痫发作风险（3.34，[1.87，5.95]，0.69，<0.001）在将先前自我报告的癫痫发作添加到模型中时仍然显着。未发现与医疗依从性相关。在电子调查反应与随后的EEG癫痫发作之间没有发现显着关联。
结论：我们的结果表明，患者可能倾向于在连续分组中发生的自我预测癫痫发作，情绪低落和压力增加可能是以前癫痫发作的结果，而不是独立的先兆症状。并发EEG的小队列患者没有自我预测EEG癫痫发作的能力。从OR到AUC值的转换有助于直接比较调查和涉及调查预感和预测的设备研究之间的性能。
方法：收集有和没有并发脑电图记录的患者的长期电子调查。从电子调查中获得的信息包括药物依从性，睡眠质量,心情,压力，调查前感知的癫痫发作风险和癫痫发作发生情况。确定了EEG癫痫发作。使用单变量和多变量广义线性混合效应回归模型来估计比值比（OR）以评估关系。使用将OR转换为曲线下等效面积（AUC）的数学公式将结果与癫痫发作预测分类器和设备预测文献进行比较。
结果：54名受试者返回了10,269个电子调查条目，四名受试者同时采集脑电图记录。单因素分析显示应激增加(OR=2.01，95%CI=[1.12，3.61]，AUC=0.61，p=0.02）与未来自我报告癫痫发作的相对几率增加相关。多变量分析表明，以前自我报告的癫痫发作（5.37，[3.53，8.16]，0.76，<0.001）与未来自我报告的癫痫发作和高感知的癫痫发作风险（3.34，[1.87，5.95]，0.69，<0.001）在将先前自我报告的癫痫发作添加到模型中时仍然显着。未发现与医疗依从性相关。在电子调查反应与随后的EEG癫痫发作之间没有发现显着关联。
结论：我们的结果表明，患者可能倾向于在连续分组中发生的自我预测癫痫发作，情绪低落和压力增加可能是以前癫痫发作的结果，而不是独立的先兆症状。并发EEG的小队列患者没有自我预测EEG癫痫发作的能力。从OR到AUC值的转换有助于直接比较调查和涉及调查预感和预测的设备研究之间的性能。
