Abstract：
OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG).
METHODS: We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC).
RESULTS: On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments.
CONCLUSIONS: The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep.
CONCLUSIONS: Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.
METHODS: We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC).
RESULTS: On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments.
CONCLUSIONS: The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep.
CONCLUSIONS: Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.
摘要：
目的：癫痫发生区（EZ）的间期生物标志物及其在机器学习模型中的应用为改善癫痫手术评估开辟了有希望的途径。目前,大多数研究将他们的分析限制在颅内脑电图（iEEG）的短段。
方法：我们使用来自25名患者的2381小时iEEG数据，系统地选择了各种发作间条件下的5分钟片段。然后，我们使用在这些单独的段内或在它们之间计算的iEEG特征测试了EZ定位的机器学习模型,并通过精确度-召回曲线(PRAUC)下的面积评估了性能.
结果：平均而言，模型得分为0.421(机会分类器的结果为0.062).然而,PRAUC各段差异显著(0.323-0.493)。总的来说,NREM睡眠得分最高，在N2中的最佳结果为0.493。使用所有段中的数据时，该模型的性能明显优于单段，除了NREM睡眠段。
结论：基于一小段iEEG记录的模型可以获得与基于延长记录的模型相似的结果。被分析的部分应该，然而,仔细和系统地选择，最好从NREM睡眠。
结论：随机选择短iEEG段可能会导致EZ定位不准确。
方法：我们使用来自25名患者的2381小时iEEG数据，系统地选择了各种发作间条件下的5分钟片段。然后，我们使用在这些单独的段内或在它们之间计算的iEEG特征测试了EZ定位的机器学习模型,并通过精确度-召回曲线(PRAUC)下的面积评估了性能.
结果：平均而言，模型得分为0.421(机会分类器的结果为0.062).然而,PRAUC各段差异显著(0.323-0.493)。总的来说,NREM睡眠得分最高，在N2中的最佳结果为0.493。使用所有段中的数据时，该模型的性能明显优于单段，除了NREM睡眠段。
结论：基于一小段iEEG记录的模型可以获得与基于延长记录的模型相似的结果。被分析的部分应该，然而,仔细和系统地选择，最好从NREM睡眠。
结论：随机选择短iEEG段可能会导致EZ定位不准确。
