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Abstract：
Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of contextual factors, including age and sleep stage. Our objectives were to validate prior work on an independent data set suggesting that deep learning methods can discriminate between normal vs abnormal EEGs, to understand whether age and sleep stage information can improve discrimination, and to understand what factors lead to errors.
We train a deep convolutional neural network on a heterogeneous set of 8522 routine EEGs from the Massachusetts General Hospital. We explore several strategies for optimizing model performance, including accounting for age and sleep stage.
The area under the receiver operating characteristic curve (AUC) on an independent test set (n = 851) is 0.917 marginally improved by including age (AUC = 0.924), and both age and sleep stages (AUC = 0.925), though not statistically significant.
The model architecture generalizes well to an independent dataset. Adding age and sleep stage to the model does not significantly improve performance.
Insights learned from misclassified examples, and minimal improvement by adding sleep stage and age suggest fruitful directions for further research.
We train a deep convolutional neural network on a heterogeneous set of 8522 routine EEGs from the Massachusetts General Hospital. We explore several strategies for optimizing model performance, including accounting for age and sleep stage.
The area under the receiver operating characteristic curve (AUC) on an independent test set (n = 851) is 0.917 marginally improved by including age (AUC = 0.924), and both age and sleep stages (AUC = 0.925), though not statistically significant.
The model architecture generalizes well to an independent dataset. Adding age and sleep stage to the model does not significantly improve performance.
Insights learned from misclassified examples, and minimal improvement by adding sleep stage and age suggest fruitful directions for further research.
摘要：
脑电图（EEG）是神经系统疾病患者医学评估的核心部分。训练一种算法来标记脑电图正常和异常似乎很有挑战性，由于脑电图的异质性和上下文因素的依赖性，包括年龄和睡眠阶段。我们的目标是在一个独立的数据集上验证之前的工作，这表明深度学习方法可以区分正常和异常的EEG。了解年龄和睡眠阶段信息是否可以改善歧视，并了解哪些因素会导致错误。
我们在马萨诸塞州总医院的8522个常规EEG的异构集上训练深度卷积神经网络。我们探索了几种优化模型性能的策略，包括年龄和睡眠阶段。
独立测试集（n=851）上的受试者工作特征曲线（AUC）下面积通过包括年龄（AUC=0.924）略有改善，年龄和睡眠阶段（AUC=0.925），虽然没有统计学意义。
模型架构可以很好地推广到独立的数据集。将年龄和睡眠阶段添加到模型中不会显着提高性能。
从错误分类的例子中学到的见解，通过增加睡眠阶段和年龄，最小的改善为进一步的研究提供了有益的方向。
我们在马萨诸塞州总医院的8522个常规EEG的异构集上训练深度卷积神经网络。我们探索了几种优化模型性能的策略，包括年龄和睡眠阶段。
独立测试集（n=851）上的受试者工作特征曲线（AUC）下面积通过包括年龄（AUC=0.924）略有改善，年龄和睡眠阶段（AUC=0.925），虽然没有统计学意义。
模型架构可以很好地推广到独立的数据集。将年龄和睡眠阶段添加到模型中不会显着提高性能。
从错误分类的例子中学到的见解，通过增加睡眠阶段和年龄，最小的改善为进一步的研究提供了有益的方向。
