Abstract：Sleep staging is of great significance for diagnosis and treatment of sleep problems. Currently, existing deep sleep staging networks have limitations including low data efficiency and data distribution differences, resulting in a decrease in the model′s performance trained on the actual data from training sets. To address this problem, this paper proposed an adversarial domain adaptation network for cross-domain automatic sleep staging using single-channel EEG. This network used a feature extractor to extract EEG features, and at the same time used a non-shared attention mechanism to preserve key information of specific domains, combined a domain discriminator to align the source and target domains, and solved the class-level alignment problem through a dual-stage classifier based on iterative self-training. To verify the reliability of the proposed network, 39, 42, and 44 records were randomly selected from three public datasets of Sleep-EDF-20, SHHS1, and SHHS2 respectively. Experiments conducted on the six generated cross-domain scenarios showed that the proposed network achieved an average accuracy of 74.29% and an average MF1 value of 61.95%. When compared with the performance of other baseline models, the average accuracy of the proposed network was at least 2.01% higher than that of the existing baseline models, and the average MF1 score was at least 2.22% higher. This method provided a solution for addressing the domain shift problem in the sleep staging task.
[1] Perez-Pozuelo I, Zhai B, Palotti J, et al. The future of sleep health: a data-driven revolution in sleep science and medicine [J]. NPJ Digital Medicine, 2020, 3(1): 42.
[2] Richard B. Berry, Claude L. Albertario, et al. The American Academy of Sleep Medicine (AASM) manual for the scor-ing of sleep and associated events:rules, terminology and technical specifications [J]. Journal of Clinical Sleep Medicine, 2018 8(5): 597-619.
[3] 张远.面向睡眠健康的感知、计算和干预[J].科学通报,2022,67(1):27-38.
[4] Almas S, Wahid F, Ali S, et al. Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder [J]. Scientific Reports, 2025, 15(2554): 1-22.
[5] Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network [J]. Nature Medicine, 2019, 25(1): 65-69.
[6] Hagiwara Y, Raghavendra U, et al. A deep learning approach for Parkinson′s disease diagnosis from EEG signals [J]. Neural Computing & Applications, 2020, 32(15): 10927-10933.
[7] Ying S, Li P, Chen J, et al. An EEG-based single-channel dual-stream automatic sleep staging network with transfer learning [J]. Applied Soft Computing, 2025, 170(15): 112722.
[8] Heremans ERM, Van denBulcke L. Automated remote sleep monitoring needs uncertainty quantification [J]. Journal of Sleep Research, 2024, 34(1): 14300.
[9] Waters HS, Clifford DG. Physics-informed transfer learning to enhance sleep staging [J]. IEEE Transactions on Biomedical Engineering, 2024, 71(5): 1599-1606.
[10] 李湘喆,王丹,张柏雯,等.基于脑电图的通道选择综述 [J]. 生物医学工程学杂志,2024,41(2): 398-405.
[11] Stanislas C, Mathieu NG, Alexandre G. Domain adaptation with optimal transport improves EEG sleep stage classifiers [C]//2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI), Singapore: IEEE, 2018:1-4.
[12] Nasiri S, Clifford GD. Attentive adversarial network for large-scale sleep staging [J]. Proceedings of Machine Learning Research, 2020, 126: 457-478.
[13] He Z, Tang M, Wang P, et al. Cross-scenario automatic sleep stage classification using transfer learning and single-channel EEG [J]. Biomedical Signal Processing and Control, 2023, 81: 104501.
[14] Goldberger AL, Amaral LAN, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet-components of a new research resource for complex physiologic signals [J]. Circulation, 2000, 101(23): E215-E220.
[15] Quan SF, Howard BV, Iber C, et al. The Sleep Heart Health Study: design, rationale, and methods [J]. Sleep, 1997, 20(12): 1077-1085.
[16] Zhang GQ, Cui L, Mueller R, et al. The National Sleep Research Resource: towards a sleep data commons [J]. Journal of the American Medical Informatics Association, 2018, 25(10): 1351-1358.
[17] 时旺军,王晶,宁晓军,等.小样本场景下的元迁移学习睡眠分期模型[J].计算机应用,2024,44(5):1445-1451.
[18] Liu Z, Luo S, Lu Y, et al. Extracting multi-scale and salient features by MSE based U-Structure and CBAM for sleep staging[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 31-38.
[19] Heremans ERM, Phan H. From unsupervised to semi-supervised adversarial domain adaptation in electroencephalography-based sleep staging [J]. Journal of Neural Engineering. 2022, 19(3): 44-60.
[20] Wilson G, Cook DJ. A survey of unsupervised deep domain adaptation [J]. ACM Transactions on Intelligent Systems and Technology, 2020, 11(5): 1-46.
[21] Yoo C, Lee HW, Kang JW. Transferring structured knowledge in unsupervised domain adaptation of a sleep staging network [J]. IEEE Journal of Biomedical and Health Informatics, 2022, 26(3): 1273-1284.
[22] Zhang H, Goodfellow I, Metaxas D, et al. Self-attention generati-ve adversarial networks [EB/OL]. https://arxiv.org/abs/1805.08318,2019-07-14/2022-12-25.
[23] Eldele E, Ragab M, et al. ADAST: attentive cross-domain EEG-based sleep staging framework with iterative self-training [J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, 7(1): 210-221.
[24] Ganin Y, Ustinova E, et al. Domain-adversarial training of neural networks [J]. Journal of Machine Learning Research, 2016, 17(1): 1-35.
[25] Tzeng E, Hoffman J, Saenko K, et al. Adversarial discriminative domain adaptation [J]. IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2962-2971.
[26] Long M, Cao Z, Wang J, et al. Conditional adversarial domain adaptation [J]. Advances in Neural Information Processing Systems, 2018, 31: 1640-1650.
[27] Yaroslav G, Evgeniya U, Hana A, et al. Domain-Adversarial Training of Neural Networks[EB/OL]. .https://arxiv.org/abs/1505.07818, 2016-05-26/2022-12-25.
[28] 胡凯蕾,陈景霞,张鹏伟,等.用于睡眠精准分期的多模态生理时频特征提取网络 [J]. 生物医学工程学杂志, 2024, 41(1): 26-33.
[29] Zhou, G., Fan, Y., Shi, J.,et al. Conditional Generative Adversarial Networks for Domain Transfer: A Survey[J]. Applied Sciences. 2022, 12(16): 8350.
[30] Stanley N. The future of sleep staging, revisited [J]. Nat Sci Sleep,2023, 15: 313-322.
