Representação por grafos do comportamento visual humano na avaliação da dor neonatal
Resumo
Este estudo analisa a dinâmica do comportamento visual por meio de modelagem em grafos, representando Áreas de Interesse como nós e as transições entre elas como arestas. Dados de 102 participantes (44 médicos, 29 leigos e 29 pais) foram coletados usando rastreamento ocular durante a avaliação de faces neonatais para a inferência de dor. Quatro regras de agregação foram comparadas utilizando métricas de rede. Médicos exibiram estratégias mais focadas, com grafos mais esparsos e organizados, priorizando o sulco nasolabial, ao passo que pais e leigos apresentaram padrões mais dispersos. Esses resultados evidenciam o potencial da abordagem em grafos para revelar diferenças cognitivas na avaliação visual da dor neonatal.Referências
Balda, R. d. C. X., Guinsburg, R., de Almeida, M. F. B., de Araújo Peres, C., Miyoshi, M. H., and Kopelman, B. I. (2000). The recognition of facial expression of pain in full-term newborns by parents and health professionals. Archives of pediatrics & adolescent medicine, 154(10):1009–1016.
Bandara, N. S., Kandappu, T., Sen, A., Gokarn, I., and Misra, A. (2024). Eyegraph: Modularity-aware spatio temporal graph clustering for continuous event-based eye tracking. In Neural Information Processing Systems.
de Magalhães Júnior, R. G., Orsi, R. N., Heiderich, T. M., de Moraes Barros, M. C., Guinsburg, R., and Thomaz, C. E. (2025). Visual and pupillary behavior in neonatal pain assessment using eye-tracking. IEEE Latin America Transactions, 23(10):931–937.
de Magalhães Júnior, R. G. (2026). Percepção e cognição humana na avaliação visual da dor neonatal usando rastreamento ocular. Tese (doutorado em engenharia elétrica), Centro Universitário FEI, São Bernardo do Campo, SP.
Endriss, U. and Grandi, U. (2018). Graph aggregation. In Companion Proceedings of the The Web Conference 2018, pages 447–450.
Fortunato, S. and Castellano, C. (2012). Community structure in graphs. In Computational complexity, pages 490–512. Springer.
Ghalmane, Z., Cherifi, C., Cherifi, H., and Hassouni, M. E. (2021). Extracting modular-based backbones in weighted networks. Inf. Sci., 576:454–474.
Gu, Y., Wang, C., Bixler, R., and D’Mello, S. (2017). Etgraph: A graph-based approach for visual analytics of eye-tracking data. Computers & Graphics, 62:1–14.
Heiderich, T. M., Leslie, A. T. F. S., and Guinsburg, R. (2015). Neonatal procedural pain can be assessed by computer software that has good sensitivity and specificity to detect facial movements. Acta Paediatrica, 104(2):e63–e69.
Hummel, P., Puchalski, M., Creech, S., and Weiss, M. (2008). Clinical reliability and validity of the n-pass: neonatal pain, agitation and sedation scale with prolonged pain. Journal of perinatology, 28(1):55–60.
Mahanama, B., Jayawardana, Y., Rengarajan, S., Jayawardena, G., Chukoskie, L., Snider, J., and Jayarathna, S. (2022). Eye movement and pupil measures: A review. Frontiers in Computer Science, 3:733531.
McCain, K. (2021). Majority rules? (group belief aggregation).
Melnyk, K., Friedman, L., and Komogortsev, O. V. (2024). What can entropy metrics tell us about the characteristics of ocular fixation trajectories? Plos one, 19(1):e0291823.
Newman, M. E. (2010). Networks: an introduction.[sl]: Oxford university press, 2010.
Orsi, R. N., Carlini, L. P., Heiderich, T. M., da Silva, G. V. T., Soares, J. d. C. A., Balda, R. d. C. X., Barros, M. C. d. M., Guinsburg, R., and Thomaz, C. E. (2023). Visual attention during neonatal pain assessment: A 2-s exposure to a facial expression is sufficient. Electronics Letters, 59(6):e12756.
Raju, M. H., Friedman, L., Lohr, D. J., and Komogortsev, O. V. (2024). Temporal persistence and intercorrelation of embeddings learned by an end-to-end deep learning eye movement-driven biometrics pipeline. arXiv preprint arXiv:2402.16399.
Saramäki, J., Kivelä, M., Onnela, J.-P., Kaski, K., and Kertesz, J. (2007). Generalizations of the clustering coefficient to weighted complex networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 75(2):027105.
Tamanaka, F. G., Carlini, L. P., Heiderich, T. M., Balda, R. C., Barros, M. C., Guinsburg, R., and Thomaz, C. E. (2023). Neonatal pain assessment: A kendall analysis between clinical and visually perceived facial features. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(3):331–340.
Vasudev, C. (2006). Graph theory with applications. New Age International.
Vehlen, A., Standard, W., and Domes, G. (2022). How to choose the size of facial areas of interest in interactive eye tracking. PLoS One, 17(2):e0263594.
Walter, J. L., Schmidt, V., König, S. U., and König, P. (2023). Navigating virtual worlds: Examining spatial navigation using a graph theoretical analysis of eye tracking data recorded in virtual reality. In Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, pages 1–2.
Xia, Y., Luo, J., Lan, M., Zhou, G., Li, Z., and Liu, S. (2023). Reason more like human: Incorporating meta information into hierarchical reinforcement learning for knowledge graph reasoning. Applied Intelligence, 53(11):13293–13308.
Yang, K.-F. and Li, Y. (2025). Visual attention graph. ArXiv, abs/2503.08531.
Yassin, A., Haidar, A., Cherifi, H., Seba, H., and Togni, O. (2023). An evaluation tool for backbone extraction techniques in weighted complex networks. Scientific Reports, 13(1):17000.
Yazdan-Shahmorad, P., Sammaknejad, N., and Bakouie, F. (2020). Graph-based analysis of visual scanning patterns: A developmental study on green and normal images. Scientific Reports, 10(1):7791.
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., and Sun, M. (2018). Graph neural networks: A review of methods and applications. ArXiv, abs/1812.08434.
Bandara, N. S., Kandappu, T., Sen, A., Gokarn, I., and Misra, A. (2024). Eyegraph: Modularity-aware spatio temporal graph clustering for continuous event-based eye tracking. In Neural Information Processing Systems.
de Magalhães Júnior, R. G., Orsi, R. N., Heiderich, T. M., de Moraes Barros, M. C., Guinsburg, R., and Thomaz, C. E. (2025). Visual and pupillary behavior in neonatal pain assessment using eye-tracking. IEEE Latin America Transactions, 23(10):931–937.
de Magalhães Júnior, R. G. (2026). Percepção e cognição humana na avaliação visual da dor neonatal usando rastreamento ocular. Tese (doutorado em engenharia elétrica), Centro Universitário FEI, São Bernardo do Campo, SP.
Endriss, U. and Grandi, U. (2018). Graph aggregation. In Companion Proceedings of the The Web Conference 2018, pages 447–450.
Fortunato, S. and Castellano, C. (2012). Community structure in graphs. In Computational complexity, pages 490–512. Springer.
Ghalmane, Z., Cherifi, C., Cherifi, H., and Hassouni, M. E. (2021). Extracting modular-based backbones in weighted networks. Inf. Sci., 576:454–474.
Gu, Y., Wang, C., Bixler, R., and D’Mello, S. (2017). Etgraph: A graph-based approach for visual analytics of eye-tracking data. Computers & Graphics, 62:1–14.
Heiderich, T. M., Leslie, A. T. F. S., and Guinsburg, R. (2015). Neonatal procedural pain can be assessed by computer software that has good sensitivity and specificity to detect facial movements. Acta Paediatrica, 104(2):e63–e69.
Hummel, P., Puchalski, M., Creech, S., and Weiss, M. (2008). Clinical reliability and validity of the n-pass: neonatal pain, agitation and sedation scale with prolonged pain. Journal of perinatology, 28(1):55–60.
Mahanama, B., Jayawardana, Y., Rengarajan, S., Jayawardena, G., Chukoskie, L., Snider, J., and Jayarathna, S. (2022). Eye movement and pupil measures: A review. Frontiers in Computer Science, 3:733531.
McCain, K. (2021). Majority rules? (group belief aggregation).
Melnyk, K., Friedman, L., and Komogortsev, O. V. (2024). What can entropy metrics tell us about the characteristics of ocular fixation trajectories? Plos one, 19(1):e0291823.
Newman, M. E. (2010). Networks: an introduction.[sl]: Oxford university press, 2010.
Orsi, R. N., Carlini, L. P., Heiderich, T. M., da Silva, G. V. T., Soares, J. d. C. A., Balda, R. d. C. X., Barros, M. C. d. M., Guinsburg, R., and Thomaz, C. E. (2023). Visual attention during neonatal pain assessment: A 2-s exposure to a facial expression is sufficient. Electronics Letters, 59(6):e12756.
Raju, M. H., Friedman, L., Lohr, D. J., and Komogortsev, O. V. (2024). Temporal persistence and intercorrelation of embeddings learned by an end-to-end deep learning eye movement-driven biometrics pipeline. arXiv preprint arXiv:2402.16399.
Saramäki, J., Kivelä, M., Onnela, J.-P., Kaski, K., and Kertesz, J. (2007). Generalizations of the clustering coefficient to weighted complex networks. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 75(2):027105.
Tamanaka, F. G., Carlini, L. P., Heiderich, T. M., Balda, R. C., Barros, M. C., Guinsburg, R., and Thomaz, C. E. (2023). Neonatal pain assessment: A kendall analysis between clinical and visually perceived facial features. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(3):331–340.
Vasudev, C. (2006). Graph theory with applications. New Age International.
Vehlen, A., Standard, W., and Domes, G. (2022). How to choose the size of facial areas of interest in interactive eye tracking. PLoS One, 17(2):e0263594.
Walter, J. L., Schmidt, V., König, S. U., and König, P. (2023). Navigating virtual worlds: Examining spatial navigation using a graph theoretical analysis of eye tracking data recorded in virtual reality. In Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, pages 1–2.
Xia, Y., Luo, J., Lan, M., Zhou, G., Li, Z., and Liu, S. (2023). Reason more like human: Incorporating meta information into hierarchical reinforcement learning for knowledge graph reasoning. Applied Intelligence, 53(11):13293–13308.
Yang, K.-F. and Li, Y. (2025). Visual attention graph. ArXiv, abs/2503.08531.
Yassin, A., Haidar, A., Cherifi, H., Seba, H., and Togni, O. (2023). An evaluation tool for backbone extraction techniques in weighted complex networks. Scientific Reports, 13(1):17000.
Yazdan-Shahmorad, P., Sammaknejad, N., and Bakouie, F. (2020). Graph-based analysis of visual scanning patterns: A developmental study on green and normal images. Scientific Reports, 10(1):7791.
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., and Sun, M. (2018). Graph neural networks: A review of methods and applications. ArXiv, abs/1812.08434.
Publicado
01/06/2026
Como Citar
MAGALHÃES JUNIOR, Roberto; ORSI, Rafael; HEIDERICH, Tatiany Marcondes; BARROS, Marina C. M.; GUINSBURG, Ruth; THOMAZ, Carlos Eduardo.
Representação por grafos do comportamento visual humano na avaliação da dor neonatal. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 133-144.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2026.20409.
