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English Information

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Authors
# Name
1 Fernando Soster(fernando.soster@usp.br)
2 Letícia Soster(leticia.soster@hc.fm.usp.br)
3 Mirela Cazzolato(mtcazzolato@gmail.com)

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Reference
# Reference
1 Alvarez-Estevez, D. and Rijsman, R. (2022). Haaglanden Medisch Centrum sleep staging database. PhysioNet. Version 1.1, DOI: 10.13026/t79q-fr32.
2 American Academy of Sleep Medicine (2023). The AASM Manual for the Scoring of Sleep and Associated Events: Terminology and Technical Specifications. Version 3.
3 Anderer, P., Ross, M., Cerny, A., Vasko, R., Shaw, E., and Fonseca, P. (2023). Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing. Front. Sleep, 2:1163477. DOI: 10.3389/frsle.2023.1163477.
4 Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., and Gramfort, A. (2018). A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26:758–769. DOI: 10.1109/TNSRE.2018.2813138.
5 Chen, X., Zhang, Y., Tang, Z., Wu, H., Chen, C., and Chen, W. (2025). MtRBD: Advancing iRBD Analysis With Multi-Task Learning for Joint Sleep Staging and RSWA Detection. IEEE J. Biomed. Health Inform., 29(12):8743–8750. DOI: 10.1109/JBHI.2025.3606944.
6 Frauscher, B., Iranzo, A., et al. (2012). Normative emg values during rem sleep for the diagnosis of rem sleep behavior disorder. Sleep, 35(6):835–847.
7 Goerttler, S., Wang, Y., Eldele, E., Wu, M., and He, F. (2025). MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification. DOI: 10.48550/arXiv.2501.02949.
8 Gu, A. and Dao, T. (2024). Linear-Time Sequence Modeling with Selective State Spaces. DOI: 10.48550/arXiv.2312.00752.
9 Jiahao, H., Ur Rahman, M. M., Al-Naffouri, T., and Laleg-Kirati, T.-M. (2025). MambaCAM-Sleep: A Mamba-based Channel Attention Model for Sleep Staging Classification. In Annual Int. Conf. of the IEEE EMBC, pages 1–6, Copenhagen, Denmark. IEEE. DOI: 10.1109/EMBC58623.2025.11251806.
10 Postuma, R. B., Iranzo, A., et al. (2019). Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study. Brain, 142(3):744–759. DOI: 10.1093/brain/awz030.
11 Schenck, C. H., Bundlie, S. R., Ettinger, M. G., and Mahowald, M. W. (1986). Chronic Behavioral Disorders of Human REM Sleep: A New Category of Parasomnia. Sleep, 9(2):293–308. DOI: 10.1093/sleep/9.2.293.
12 Sobreira-Neto, M. A., Stelzer, F. G., et al. (2023). REM sleep behavior disorder: update on diagnosis and management. Arquivos de Neuro-Psiquiatria, 81(12):1179–1194. DOI: 10.1055/s-0043-1777111.
13 Supratak, A., Dong, H., Wu, C., and Guo, Y. (2017). DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel eeg. IEEE TNSRE Journal, 25(11):1998–2008. DOI: 10.1109/TNSRE.2017.2721116.
14 Tsinalis, O., Matthews, P. M., and Guo, Y. (2016). Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Annals of Biomedical Engineering, 44:1587–1597. DOI: 10.1007/s10439-015-1444-y.
15 Zhang, A. H., He-Mo, A., et al. (2026). Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography. SLEEP, 49(4):zsag022. DOI: 10.1093/sleep/zsag022.
16 Zhang, C., Cui, W., and Guo, J. (2024). MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba. DOI: 10.48550/arXiv.2405.20142.
17 Zhou, X., Han, Y., et al. (2024). BiT-MamSleep: Bidirectional temporal Mamba for eeg sleep staging. URL: http://arxiv.org/abs/2411.01589.