Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography.
Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography.
复制标题
基于单通道脑电图的自动睡眠分期中的深度学习
DOI：
10.3389/fphys.2021.628502
复制
发表时间：
2021
影响因子：
4
通讯作者：
Hou F
中科院分区：
文献类型：
--
作者：
Fu M;Wang Y;Chen Z;Li J;Xu F;Liu X;Hou F
本研究以单通道脑电图（EEG）为中心，对睡眠分期进行了自动化研究，并取得了一些有意义的结果。在这项研究中，我们提出了一个基于深度学习的网络，通过整合注意力机制和双向长短期记忆神经网络（AT-BiLSTM）来分类觉醒，快速眼动（REM）睡眠和非REM（NREM）睡眠阶段N1，N2和N3。AT-BiLSTM网络的表现优于其他五个网络，在PhysioNet Sleep-EDF Expanded数据集上实现了83.78%的准确率，Cohen's kappa系数为0.766，宏观F1得分为82.14%，在DREAMS Subjects数据集上实现了81.72%的准确率，Cohen's kappa系数为0.751，宏观F1得分为80.74%。所提出的AT-BiLSTM网络甚至比现有的基于传统特征提取的方法实现了更高的准确性。此外，与位于中央，枕叶或顶叶的EEG通道相比，使用额叶EEG推导的AT-BiLSTM网络获得了更好的性能。由于EEG信号可以很容易地使用干电极在前额上采集，我们的研究结果可能会提供一个很有前途的解决方案，自动睡眠评分没有特征提取，并可能被证明是非常有用的睡眠障碍的筛查。
This study centers on automatic sleep staging with a single channel electroencephalography (EEG), with some significant findings for sleep staging. In this study, we proposed a deep learning-based network by integrating attention mechanism and bidirectional long short-term memory neural network (AT-BiLSTM) to classify wakefulness, rapid eye movement (REM) sleep and non-REM (NREM) sleep stages N1, N2 and N3. The AT-BiLSTM network outperformed five other networks and achieved an accuracy of 83.78%, a Cohen’s kappa coefficient of 0.766 and a macro F1-score of 82.14% on the PhysioNet Sleep-EDF Expanded dataset, and an accuracy of 81.72%, a Cohen’s kappa coefficient of 0.751 and a macro F1-score of 80.74% on the DREAMS Subjects dataset. The proposed AT-BiLSTM network even achieved a higher accuracy than the existing methods based on traditional feature extraction. Moreover, better performance was obtained by the AT-BiLSTM network with the frontal EEG derivations than with EEG channels located at the central, occipital or parietal lobe. As EEG signal can be easily acquired using dry electrodes on the forehead, our findings might provide a promising solution for automatic sleep scoring without feature extraction and may prove very useful for the screening of sleep disorders.
登录
查看更多内容
影响因子：
8.8
作者：
Hassan, Ahnaf Rashik;Subasi, Abdulhamit
通讯作者：
Subasi, Abdulhamit
影响因子：
3
作者：
Hassan, Ahnaf Rashik;Bhuiyan, Mohammed Imamul Hassan
通讯作者：
Bhuiyan, Mohammed Imamul Hassan
影响因子：
6.1
作者：
Hassan, Ahnaf Rashik;Bhuiyan, Mohammed Imamul Hassan
通讯作者：
Bhuiyan, Mohammed Imamul Hassan
影响因子：
3.2
作者：
Bresch E;Großekathöfer U;Garcia-Molina G
通讯作者：
Garcia-Molina G
影响因子：
3
作者：
Ghimatgar, Hojat;Kazemi, Kamran;Aarabi, Ardalan
通讯作者：
Aarabi, Ardalan