Electrical and Computer Engineering, University of Maryland, College Park, MD, 20740, USA.
Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, 20993, USA.
Sci Rep. 2020 Aug 26;10(1):14206. doi: 10.1038/s41598-020-70569-y.
The amount of freely available human phenotypic data is increasing daily, and yet little is known about the types of inferences or identifying characteristics that could reasonably be drawn from that data using new statistical methods. One data type of particular interest is electroencephalographical (EEG) data, collected noninvasively from humans in various behavioral contexts. The Temple University EEG corpus associates thousands of hours of de-identified EEG records with contemporaneous physician reports that include metadata that might be expected to show a measurable correlation with characteristics of the recorded signal. Given that machine learning methods applied to neurological signals are being used in emerging diagnostic applications, we leveraged this data source to test the confidence with which algorithms could predict, using a patient's EEG record(s) as input, which medications were noted on the matching physician report. We comparatively assessed deep learning and feature-based approaches on their ability to distinguish between the assumed presence of Dilantin (phenytoin), Keppra (levetiracetam), or neither. Our methods could successfully distinguish between patients taking either anticonvulsant and those taking no medications; as well as between the two anticonvulsants. Further, we found different approaches to be most effective for different groups of classifications.
自由可用的人类表型数据量每天都在增加，但对于使用新的统计方法可以从这些数据中合理推断出哪些类型的推断或识别特征，人们知之甚少。一种特别有趣的数据类型是脑电图 (EEG) 数据，它从各种行为环境中以非侵入性方式从人体采集。坦普尔大学 EEG 语料库将数千小时的去识别 EEG 记录与同时期的医生报告相关联，其中包含可能与记录信号特征有一定相关性的元数据。鉴于应用于神经信号的机器学习方法正在新兴的诊断应用中得到应用，我们利用这一数据源，测试算法使用患者的 EEG 记录作为输入，根据匹配医生报告来预测患者正在服用哪种药物的置信度。我们比较了深度学习和基于特征的方法在区分假设的苯妥英（苯妥英钠）、开浦兰（左乙拉西坦）或两者都不存在的能力。我们的方法可以成功地区分服用抗癫痫药物的患者和未服用药物的患者；以及两种抗癫痫药物。此外，我们发现不同的方法对不同类别的分类最有效。
