SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Maria Yohanne Moreira(yohannemoreira@insightlab.ufc.br)
2 Vinicius Monteiro de Lira(vinicius.monteiro@insightlab.ufc.br)
3 Livia Almada Cruz(livia@insightlab.ufc.br)
4 José Macêdo(jose.macedo@insightlab.ufc.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Sekkal, R. N., Bereksi-Reguig, F., Ruiz-Fernandez, D., Dib, N., and Sekkal, S. (2022). Automatic sleep stage classification: From classical machine learning methods to deep learning. Biomedical Signal Processing and Control, 77:103751.
2 Birrer, V., Elgendi, M., Lambercy, O., and Menon, C. (2024). Evaluating reliability in wearable devices for sleep staging. NPJ Digital Medicine, 7(1):74.
3 Walch, O., Huang, Y., Forger, D., and Goldstein, C. (2019). Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device. Sleep, 42(12):zsz180.
4 Chaparro-Vargas, R. and Cvetkovic, D. (2013). A single-trial toolbox for advanced sleep polysomnographic preprocessing. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 5829–5832. IEEE.
5 Charlton, P. H., Marozas, V., Mejía-Mejía, E., Kyriacou, P. A., and Mant, J. (2025). De­terminants of photoplethysmography signal quality at the wrist. PLOS Digital Health, 4(6):e0000585.
6 Wang, Q., Zhao, D., Wang, Y., et al. (2019). Ensemble learning algorithm based on multi-parameters for sleep staging. Medical & Biological Engineering & Computing, 57(8):1693–1707.
7 Chen, X., Xu, X., Liu, A., Lee, S., Chen, X., Zhang, X., McKeown, M. J., and Wang, Z. J. (2019). Removal of muscle artifacts from the eeg: A review and recommendations. IEEE Sensors Journal, 19(14):5353–5368.
8 Chriskos, P., Frantzidis, C. A., Gkivogkli, P. T., Bamidis, P. D., and Kourtidou-Papadeli, C. (2018). Achieving accurate automatic sleep staging on manually pre-processed eeg data through synchronization feature extraction and graph metrics. Frontiers in human neuroscience, 12:110.
9 Correa, M. A. G. and Leber, E. L. (2011). Noise removal from eeg signals in polisomno­graphic records applying adaptive filters in cascade. Adaptive filtering applications, 34:1–26.
10 de Zambotti, M., Goldstein, C., Cook, J., Menghini, L., Altini, M., Cheng, P., and Robil­lard, R. (2024). State of the science and recommendations for using wearable technol­ogy in sleep and circadian research. Sleep, 47(4):zsad325.
11 Fedorin, I. and Slyusarenko, K. (2021). Consumer smartwatches as a portable psg: Lstm based neural networks for a sleep-related physiological parameters estimation. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pages 849–452.
12 Gunter, K. M., Brink-Kjaer, A., Mignot, E., Sørensen, H. B., During, E., and Jennum, P. (2023). Svit: A spectral vision transformer for the detection of rem sleep behavior disorder. IEEE Journal of Biomedical and Health Informatics, 27(9):4285–4292.
13 Huang, X., Shirahama, K., Irshad, M. T., Nisar, M. A., Piet, A., and Grzegorzek, M. (2023). Sleep stage classification in children using self-attention and gaussian noise data augmentation. Sensors, 23(7).
14 Kim, J., Lee, J., and Shin, M. (2017). Sleep stage classification based on noise-reduced fractal property of heart rate variability. Procedia Computer Science, 116:435–440. Discovery and innovation of computer science technology in artificial intelligence era: The 2nd international Conference on Computer Science and Computational Intelli­gence (ICCSCI 2017).
15 Metsis, V., Schizas, I. D., and Marshall, G. (2015). Real-time subspace denoising of polysomnographic data. In Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pages 1–4.
16 Permana, Z. Z. R., Sari, R. I., Febriani, N. S., and Setiawan, A. W. (2023). Effect of smote for sleep stages classification using decision tree, k-nearest neighbor and random forest. In 2023 International Conference on Electrical Engineering and Informatics (ICEEI), pages 1–6.
17 Phan, H., Andreotti, F., Cooray, N., Chén, O. Y., and De Vos, M. (2019). Joint classi­fication and prediction cnn framework for automatic sleep stage classification. IEEE Transactions on Biomedical Engineering, 66(5):1285–1296.
18 Sekkal, R. N., Bereksi-Reguig, F., Ruiz-Fernandez, D., Dib, N., and Sekkal, S. (2022). Automatic sleep stage classification: From classical machine learning methods to deep learning. Biomedical Signal Processing and Control, 77:103751.
19 Walch, O., Huang, Y., Forger, D., and Goldstein, C. (2019). Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device. Sleep, 42(12):zsz180.
20 Wang, Q., Zhao, D., Wang, Y., et al. (2019). Ensemble learning algorithm based on multi-parameters for sleep staging. Medical & Biological Engineering & Computing, 57(8):1693–1707.