An electroencephalography sleep staging model based on bidirectional Mamba combining synergistic attention with interactive convolution

Abstract

Background: This study presents SIMSleepSM, a novel single-channel electroencephalography (EEG) sleep staging model. It addresses two primary challenges: insufficient modeling of long-range temporal dependencies combined with limited multi-scale feature extraction, and poor accuracy in identifying the N1 stage.

Methods: SIMSleepSM extends the SleePyCo architecture through three principal innovations. First, the spatial-channel synergistic attention (SCSA) module is adapted into a 1D variant, SCSA_1D, tailored for EEG signals and inserted into every feature layer of the backbone network. The spatial attention extracts local temporal dependencies across various time scales, whereas the channel attention captures relationships among feature channels. Together these attentions strengthen temporal dependency modeling and emphasize N1-specific features. Second, an interactive convolution block (ICB) is integrated into the feature pyramid. The ICB employs a two-branch interactive convolution to refine multi-scale feature extraction. Finally, a bidirectional Mamba-based classifier is designed. Its bidirectional state space mechanism captures long-range temporal dependencies in the EEG and thereby strengthens representation of sleep-stage dynamics.

Results: On the Sleep-EDF-20, Sleep-EDF-78, and Sleep Heart Health Study (SHHS) datasets, SIMSleepSM achieves accuracy values of 88.1%, 86.2%, and 84.1%; records macro F1 scores of 82.7%, 81.0%, and 77.9%; obtains Cohen's Kappa coefficients of 0.839, 0.810, and 0.791; and attains F1-scores on the N1 stage of 53.7%, 54.4%, and 50.9% for Sleep-EDF-20, Sleep-EDF-78, and SHHS, surpassing the second-best models by 1.3%, 4.0%, and 4.8%, respectively.

Conclusion: Experimental results demonstrate that SIMSleepSM outperforms thirteen state-of-the-art baseline models, with particularly notable improvements in N1-stage identification. These results indicate that SIMSleepSM provides an effective and reliable solution for automatic sleep staging using single-channel EEG, highlighting it as a robust and high-performing model.

Keywords

bidirectional Mamba / computer application technology / interactive convolution / single-channel EEG / sleep staging / spatial-channel synergistic attention

References

[1] Simmonds E, Levine KS, Han J, et al. Sleep disturbances as risk factors for neurodegeneration later in life. npj Demen. 2025;1(1):1-10. https://doi.org/10.1038/s44400-025-00008-0

[2] Yue H, Chen Z, Guo W, et al. Research and application of deep learning-based sleep staging: data, modeling, validation, and clinical practice. Sleep Med Rev. 2024;74:1-12. https://doi.org/10.1016/j.smrv.2024.101897

[3] Zhang X, Zhang X, Huang Q, Lv Y, Chen F. A review of automated sleep stage based on EEG signals. Biocybern Biomed Eng. 2024;44(3):651-673. https://doi.org/10.1016/j.bbe.2024.06.004

[4] Almutairi H, Hassan GM, Datta A. Machine-learning-based-approaches for sleep stage classification utilising a combination of physiological signals: a systematic review. Appl Sci. 2023;13(24):1-24. https://doi.org/10.3390/app132413280

[5] Tsinalis O, Matthews PM, Guo Y, et al. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks. arXiv preprint arXiv:1610.01683. 2016:1-12. https://arxiv.org/abs/1610.01683

[6] Jadhav P, Mukhopadhyay S. Automated sleep stage scoring using time-frequency spectra convolution neural network. IEEE Trans Instrum Meas. 2022;71:1-9. https://doi.org/10.1109/tim.2022.3177747

[7] Li C, Qi Y, Ding X, Zhao J, Sang T, Lee M. A deep learning method approach for sleep stage classification with EEG spectrogram. Int J Environ Res Publ Health. 2022;19(10):1-17. https://doi.org/10.3390/ijerph19106322

[8] Supratak A, Dong H, Wu C, Guo Y. DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng. 2017;25(11):1998-2008. https://doi.org/10.1109/tnsre.2017.2721116

[9] Phan H, Chén OY, Tran MC, Koch P, Mertins A, De Vos M. XSleepNet: multi-view sequential model for automatic sleep staging. IEEE Trans Pattern Anal Mach Intell. 2021;44(9):5903-5915. https://doi.org/10.1109/TPAMI.2021.3070057

[10] Zhang Y, Cao W, Feng L, et al. SHNN: a single-channel EEG sleep staging model based on semi-supervised learning. Expert Syst Appl. 2023;213:1-12. https://doi.org/10.1016/j.eswa.2022.119288

[11] Mousavi S, Afghah F, Acharya UR. SleepEEGNet: automated sleep stage scoring with sequence to sequence deep learning approach. PloS One. 2019;14(5):1-15. https://doi.org/10.1371/journal.pone.0216456

[12] Zhao C, Li J, Guo Y. SleepContextNet: a temporal context network for automatic sleep staging based single-channel EEG. Comput Methods Progr Biomed. 2022;220(1-12):106806. https://doi.org/10.1016/j.cmpb.2022.106806

[13] Lee S, Yu Y, Back S, Seo H, Lee K. SleePyCo: automatic sleep scoring with feature pyramid and contrastive learning. Expert Syst Appl. 2024;240:1-14. https://doi.org/10.1016/j.eswa.2023.122551

[14] Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces. In: Proceedings of the First Conference on Language Modeling. COLM; 2024.

[15] Liang A, Jiang X, Sun Y, et al. Bi-mamba+: bidirectional mamba for time series forecasting. arXiv preprint arXiv:2404.15772. 2024.

[16] Si Y, Xu H, Zhu X, et al. SCSA: exploring the synergistic effects between spatial and channel attention. Neurocomputing. 2025;634:129866. https://doi.org/10.1016/j.neucom.2025.129866

[17] Eldele E, Ragab M, Chen Z, et al. TSLANet: rethinking transformers for time series representation learning. In: Proceedings of the 41st International Conference on Machine Learning. PMLR; 2024:12409-12428.

[18] Phan H, Mikkelsen K, Chén OY, Koch P, Mertins A, De Vos M. SleepTransformer: automatic sleep staging with interpretability and uncertainty quantification. IEEE Trans Biomed Eng. 2022;69(8):2456-2467. https://doi.org/10.1109/tbme.2022.3147187

[19] Kim M, Jung K, Chung W. Automatic sleep stage classification method based on transformer-in-transformer. In: 2023 11th International Winter Conference on Brain-Computer Interface (BCI). IEEE; 2023:1-5.

[20] Li W, Liu T, Xu B, Song A. SleepFC: feature pyramid and cross-scale context learning for sleep staging. IEEE Trans Neural Syst Rehabil Eng. 2024;32:2198-2208. https://doi.org/10.1109/tnsre.2024.3406383

[21] Yang B, Wu W, Liu Y, Liu H. A novel sleep stage contextual refinement algorithm leveraging conditional random fields. IEEE Trans Instrum Meas. 2022;71:1-13. https://doi.org/10.1109/tim.2022.3154838

[22] Khosla P, Teterwak P, Wang C, et al. Supervised contrastive learning. Adv Neural Inf Process Syst. 2020;33:18661-18673.

[23] Kemp B, Zwinderman AH, Tuk B, Kamphuisen H, Oberye J. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng. 2000;47(9):1185-1194. https://doi.org/10.1109/10.867928

[24] Goldberger AL, Amaral LAN, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):215-220. https://doi.org/10.1161/01.cir.101.23.e215

[25] Quan SF, Howard BV, Iber C, et al. The sleep heart health study: design, rationale, and methods. Sleep. 1997;20(12):1077-1085.

[26] Zhang GQ, Cui L, Mueller R, et al. The national sleep research resource: towards a sleep data commons. J Am Med Inf Assoc. 2018;25(10):1351-1358. https://doi.org/10.1093/jamia/ocy064

[27] Liu P, Qian W, Zhang H, et al. Automatic sleep stage classification using deep learning: signals, data representation, and neural networks. Artif Intell Rev. 2024;57(11):1-68. https://doi.org/10.1007/s10462-024-10926-9

[28] Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manag. 2009;45(4):427-437. https://doi.org/10.1016/j.ipm.2009.03.002

[29] Kong G, Li C, Peng H, Han Z, Qiao H. EEG-based sleep stage classification via neural architecture search. IEEE Trans Neural Syst Rehabil Eng. 2023;31:1075-1085. https://doi.org/10.1109/tnsre.2023.3238764

[30] Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9:2579-2605.

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