Exploring Label Noise Reduction Techniques for Sleep Stage Classification Using Wearable Devices
Resumo
Wearable devices offer a portable alternative to polysomnography for sleep stage classification using accelerometer and photoplethysmography (PPG) data. However, the performance of machine learning models heavily depends on the quality of the reference data. Consequently, the presence of incorrectly labeled data undermines the performance of these models. In this work, we conduct an exploratory analysis of different label noise reduction methods, including duration window and Isolation Forest. We evaluate the impact of these techniques on sleep stage prediction using several machine learning classifiers. Our results provide insights into the effectiveness and characteristics of label noise reduction methods for improving sleep stage classification.
Palavras-chave:
Sleep Stage Classification, Noise reduction, Wearable Devices
Referências
Birrer, V., Elgendi, M., Lambercy, O., and Menon, C. (2024). Evaluating reliability in wearable devices for sleep staging. NPJ Digital Medicine, 7(1):74.
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.
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.
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.
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.
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.
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.
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.
Gunter, K. M., Brink-Kjær, 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.
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).
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Gunter, K. M., Brink-Kjær, 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.
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).
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).
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.
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.
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.
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.
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.
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.
Publicado
29/09/2025
Como Citar
MOREIRA, Maria Yohanne; DE LIRA, Vinicius Monteiro; ALMADA CRUZ, Livia; MACÊDO, José Antônio Fernandes de.
Exploring Label Noise Reduction Techniques for Sleep Stage Classification Using Wearable Devices. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 25-32.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2025.248075.
