Integration of Subjective and Emotional Physiological Data for Classifying Sleep Quality with Machine Learning
Resumo
A qualidade do sono é um fator crítico para a saúde física e mental, associando-se diretamente a condições como ansiedade, doenças cardiovasculares e distúrbios metabólicos. Este trabalho propõe um pipeline de aprendizado de máquina para classificar a qualidade do sono em “boa” ou “ruim”, integrando dados fisiológicos (actigrafia), subjetivos (PSQI) e emocionais (STAI-Y2). Os experimentos foram realizados sobre o conjunto MMASH com validação intersujeitos (LOSO), reforçando a generalização dos resultados. A metodologia alcançou acurácia de 0.98 ± 0.01 com uma rede neural multicamadas, superando estudos relacionados que empregaram validação menos rigorosa. Esses achados demonstram a viabilidade da combinação de múltiplas dimensões de dados para monitoramento não invasivo, com potencial aplicação em dispositivos vestíveis e intervenções personalizadas de saúde do sono.Referências
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Bitkina, O. V., Park, J., and Kim, J. (2022). Modeling sleep quality depending on objective actigraphic indicators based on machine learning methods. International Journal of Environmental Research and Public Health, 19(16):9890.
Buysse, D. J., Reynolds III, C. F., Monk, T. H., Berman, S. R., and Kupfer, D. J. (1989). The pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry research, 28(2):193–213.
Cole, R. J., Kripke, D. F., Gruen, W., Mullaney, D. J., and Gillin, J. C. (1992). Automatic sleep/wake identification from wrist activity. Sleep, 15(5):461–469.
Conte, F., De Rosa, O., Rescott, M. L., Arabia, T. P., D’Onofrio, P., Lustro, A., Malloggi, S., Molinaro, D., Spagnoli, P., Giganti, F., et al. (2022). High sleep fragmentation parallels poor subjective sleep quality during the third wave of the covid-19 pandemic: An actigraphic study. Journal of sleep research, 31(3):e13519.
Fekedulegn, D., Andrew, M. E., Shi, M., Violanti, J. M., Knox, S., and Innes, K. E. (2020). Actigraphy-based assessment of sleep parameters. Annals of Work Exposures and Health, 64(4):350–367.
Gardani, M., Bradford, D. R., Russell, K., Allan, S., Beattie, L., Ellis, J. G., and Akram, U. (2022). A systematic review and meta-analysis of poor sleep, insomnia symptoms and stress in undergraduate students. Sleep medicine reviews, 61:101565.
Geng, D., Qin, Z., Wang, J., Gao, Z., and Zhao, N. (2022). Personalized recognition of wake/sleep state based on the combined shapelets and k-means algorithm. Biomedical Signal Processing and Control, 71:103132.
Hussain, Z., Sheng, Q. Z., Zhang, W. E., Ortiz, J., and Pouriyeh, S. (2022). Non-invasive techniques for monitoring different aspects of sleep: A comprehensive review. ACM Transactions on Computing for Healthcare (HEALTH), 3(2):1–26.
Pan, Q., Brulin, D., and Campo, E. (2020). Current status and future challenges of sleep monitoring systems: Systematic review. JMIR Biomedical Engineering, 5(1):e20921.
Rossi, A., Da Pozzo, E., Menicagli, D., Tremolanti, C., Priami, C., Sirbu, A., Clifton, D., Martini, C., and Morelli, D. (2020). Multilevel monitoring of activity and sleep in healthy people (version 1.0. 0). physionet, 2020.
Sano, A., Rahman, T., Zhang, M., Ganesan, D., and Choudhury, T. (2020). Mobile sensing of alertness, sleep and circadian rhythm: Hardware & software platforms. GetMobile: Mobile Computing and Communications, 23(3):16–22.
Sathyanarayana, A., Joty, S., Fernandez-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A., Arora, T., and Taheri, S. (2016). Sleep quality prediction from wearable data using deep learning. JMIR mHealth and uHealth, 4(4):e6562.
Site, A., Lohan, E. S., Jolanki, O., Valkama, O., Hernandez, R. R., Latikka, R., Alekseeva, D., Vasudevan, S., Afolaranmi, S., Ometov, A., et al. (2022). Managing perceived loneliness and social-isolation levels for older adults: a survey with focus on wearables-based solutions. Sensors, 22(3):1108.
Angelova, M., Karmakar, C., Zhu, Y., Drummond, S. P., and Ellis, J. (2020). Automated method for detecting acute insomnia using multi-night actigraphy data. IEEE Access, 8:74413–74422.
Bitkina, O. V., Park, J., and Kim, J. (2022). Modeling sleep quality depending on objective actigraphic indicators based on machine learning methods. International Journal of Environmental Research and Public Health, 19(16):9890.
Buysse, D. J., Reynolds III, C. F., Monk, T. H., Berman, S. R., and Kupfer, D. J. (1989). The pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry research, 28(2):193–213.
Cole, R. J., Kripke, D. F., Gruen, W., Mullaney, D. J., and Gillin, J. C. (1992). Automatic sleep/wake identification from wrist activity. Sleep, 15(5):461–469.
Conte, F., De Rosa, O., Rescott, M. L., Arabia, T. P., D’Onofrio, P., Lustro, A., Malloggi, S., Molinaro, D., Spagnoli, P., Giganti, F., et al. (2022). High sleep fragmentation parallels poor subjective sleep quality during the third wave of the covid-19 pandemic: An actigraphic study. Journal of sleep research, 31(3):e13519.
Fekedulegn, D., Andrew, M. E., Shi, M., Violanti, J. M., Knox, S., and Innes, K. E. (2020). Actigraphy-based assessment of sleep parameters. Annals of Work Exposures and Health, 64(4):350–367.
Gardani, M., Bradford, D. R., Russell, K., Allan, S., Beattie, L., Ellis, J. G., and Akram, U. (2022). A systematic review and meta-analysis of poor sleep, insomnia symptoms and stress in undergraduate students. Sleep medicine reviews, 61:101565.
Geng, D., Qin, Z., Wang, J., Gao, Z., and Zhao, N. (2022). Personalized recognition of wake/sleep state based on the combined shapelets and k-means algorithm. Biomedical Signal Processing and Control, 71:103132.
Hussain, Z., Sheng, Q. Z., Zhang, W. E., Ortiz, J., and Pouriyeh, S. (2022). Non-invasive techniques for monitoring different aspects of sleep: A comprehensive review. ACM Transactions on Computing for Healthcare (HEALTH), 3(2):1–26.
Pan, Q., Brulin, D., and Campo, E. (2020). Current status and future challenges of sleep monitoring systems: Systematic review. JMIR Biomedical Engineering, 5(1):e20921.
Rossi, A., Da Pozzo, E., Menicagli, D., Tremolanti, C., Priami, C., Sirbu, A., Clifton, D., Martini, C., and Morelli, D. (2020). Multilevel monitoring of activity and sleep in healthy people (version 1.0. 0). physionet, 2020.
Sano, A., Rahman, T., Zhang, M., Ganesan, D., and Choudhury, T. (2020). Mobile sensing of alertness, sleep and circadian rhythm: Hardware & software platforms. GetMobile: Mobile Computing and Communications, 23(3):16–22.
Sathyanarayana, A., Joty, S., Fernandez-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A., Arora, T., and Taheri, S. (2016). Sleep quality prediction from wearable data using deep learning. JMIR mHealth and uHealth, 4(4):e6562.
Site, A., Lohan, E. S., Jolanki, O., Valkama, O., Hernandez, R. R., Latikka, R., Alekseeva, D., Vasudevan, S., Afolaranmi, S., Ometov, A., et al. (2022). Managing perceived loneliness and social-isolation levels for older adults: a survey with focus on wearables-based solutions. Sensors, 22(3):1108.
Publicado
29/09/2025
Como Citar
SILVA, Nicolly Alves da; OLIVEIRA, Hygo Sousa de; SOUTO, Eduardo James Pereira; GIUSTI, Rafael.
Integration of Subjective and Emotional Physiological Data for Classifying Sleep Quality with Machine Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1081-1092.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.14363.
