School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States of America.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.
PLoS One. 2019 May 7;14(5):e0216456. doi: 10.1371/journal.pone.0216456. eCollection 2019.
Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the performance of the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and κ = 0.79. Our developed model can be applied to other sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.
脑电图（EEG）是一种常用的基础信号，用于监测大脑活动和诊断睡眠障碍。手动睡眠阶段评分是睡眠专家一项耗时的任务，并且受到评分者间可靠性的限制。在本文中，我们提出了一种名为 SleepEEGNet 的基于单通道 EEG 信号的自动睡眠阶段标注方法。SleepEEGNet 由深度卷积神经网络（CNNs）组成，用于提取时不变特征、频率信息和序列到序列模型，以捕获睡眠阶段和评分之间的复杂和长期短期上下文依赖关系。此外，为了减少现有睡眠数据集呈现的类不平衡问题的影响，我们应用了新的损失函数，以使网络在训练过程中为每个睡眠阶段分配相等的错误分类误差。我们在 2013 年和 2018 年发布的 Physionet Sleep-EDF 数据集的不同单 EEG 通道（即 Fpz-Cz 和 Pz-Oz EEG 通道）上评估了所提出方法的性能。评估结果表明，与现有文献相比，所提出的方法具有最佳的标注性能，总体准确率为 84.26%，宏 F1 得分为 79.66%，κ 值为 0.79。我们开发的模型可以应用于其他睡眠 EEG 信号，并帮助睡眠专家做出准确的诊断。源代码可在 https://github.com/SajadMo/SleepEEGNet 上获得。
