Detecção de Hipóxia Fetal explorando Representações de Séries Temporais com Redes Neurais Convolucionais
Resumo
Este estudo explorou o uso do Gráfico de Recorrência (RP) e do Gráfico de Poincaré (PC) como entradas para Redes Neurais Convolucionais (CNNs) na detecção de hipóxia fetal a partir de dados de cardiotocografia. Os experimentos mostraram que o RP teve melhor desempenho geral (Sensibilidade (Se) = 61,98% ± 10,9; Especificidade (Sp) = 63,58% ± 11,2), sendo mais eficiente na detecção de padrões críticos, especialmente em segmentos de 15 minutos. O PC apresentou maior estabilidade em segmentos longos (Se = 65,62% ± 6,6; Sp = 61,17% ± 12,3), contudo, de maneira global, foi menos eficaz na identificação da hipóxia. Os resultados sugerem que o RP é mais adequado para capturar dinâmicas não lineares da frequência cardíaca fetal em sistemas automatizados de monitoramento.Referências
Ajit, A., Acharya, K., and Samanta, A. (2020). A review of convolutional neural networks. In 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), pages 1–5. IEEE.
Anwar, A., Khalifa, Y., Coyle, J. L., and Sejdic, E. (2024). Transformers in biosignal analysis: A review. Information Fusion, page 102697.
Chudáček, V., Spilka, J., Burša, M., Jankåu, P., Hruban, L., Huptych, M., and Lhotská, L. (2014). Open access intrapartum ctg database. BMC pregnancy and childbirth, 14:1–12.
Chudáček, V., Spilka, J., Lhotská, L., Janků, P., Koucký, M., Huptych, M., and Burša, M. (2011). Assessment of features for automatic ctg analysis based on expert annotation. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 6051–6054. IEEE.
Cömert, Z., Kocamaz, A. F., and Subha, V. (2018). Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment. Computers in biology and medicine, 99:85–97.
Eckmann, J.-P., Kamphorst, S. O., and Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhysics Letters, 4(9):973–977.
Faouzi, J. (2017-2021). pyts.image.recurrenceplot - pyts 0.13.0 documentation. [link]. Acesso em: 07 de mar. de 2025.
Goshvarpour, A., Goshvarpour, A., and Rahati, S. (2011). Analysis of lagged poincare plots in heart rate signals during meditation. Digital Signal Processing, 21(2):208–214.
Gunaratne, S. A., Panditharatne, S. D., and Chandraharan, E. (2022). Prediction of neonatal acidosis based on the type of fetal hypoxia observed on the cardiotocograph (ctg). European Journal of Medical and Health Sciences, 4(2):8–18.
Kanagal, D. V. and Praveen, B. (2022). Intrapartum fetal monitoring and its correlation with umbilical cord blood ph and early neonatal outcome: A prospective cohort study. Journal of South Asian Federation of Obstetrics and Gynaecology, 14(1):63–67.
Li, J., Chen, Z.-Z., Huang, L., Fang, M., Li, B., Fu, X., Wang, H., and Zhao, Q. (2018). Automatic classification of fetal heart rate based on convolutional neural network. IEEE Internet of Things Journal, 6(2):1394–1401.
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12):6999–7019.
Liang, S. and Li, Q. (2021). Automatic evaluation of fetal heart rate based on deep learning. In 2021 2nd information communication technologies conference (ICTC), pages 235–240. IEEE.
Muccini, A. M., Tran, N. T., Hale, N., McKenzie, M., Snow, R. J., Walker, D. W., and Ellery, S. J. (2022). The effects of in utero fetal hypoxia and creatine treatment on mitochondrial function in the late gestation fetal sheep brain. Oxidative Medicine and Cellular Longevity, 2022(1):3255296.
Ponsiglione, A. M., Cosentino, C., Cesarelli, G., Amato, F., and Romano, M. (2021). A comprehensive review of techniques for processing and analyzing fetal heart rate signals. Sensors, 21(18):6136.
Romano, M., Faiella, G., Bifulco, P., D’Addio, G., Clemente, F., and Cesarelli, M. (2014). Outliers detection and processing in ctg monitoring (2014) xiii mediterranean conference on medical and biological engineering and computing 2013.
Salahuddin, Z., Woodruff, H. C., Chatterjee, A., and Lambin, P. (2022). Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Computers in biology and medicine, 140:105111.
Silva, B., Ribeiro, M., and Henriques, T. S. (2022). Compression of different time series representations in asphyxia detection. In 2022 E-Health and Bioengineering Conference (EHB), pages 1–5. IEEE.
Xiao, Y., Lu, Y., Liu, M., Zeng, R., and Bai, J. (2022). A deep feature fusion network for fetal state assessment. Frontiers in Physiology, 13:969052.
Zhao, Z., Zhang, Y., Comert, Z., and Deng, Y. (2019). Computer-aided diagnosis system of fetal hypoxia incorporating recurrence plot with convolutional neural network. Frontiers in physiology, 10:255.
Anwar, A., Khalifa, Y., Coyle, J. L., and Sejdic, E. (2024). Transformers in biosignal analysis: A review. Information Fusion, page 102697.
Chudáček, V., Spilka, J., Burša, M., Jankåu, P., Hruban, L., Huptych, M., and Lhotská, L. (2014). Open access intrapartum ctg database. BMC pregnancy and childbirth, 14:1–12.
Chudáček, V., Spilka, J., Lhotská, L., Janků, P., Koucký, M., Huptych, M., and Burša, M. (2011). Assessment of features for automatic ctg analysis based on expert annotation. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 6051–6054. IEEE.
Cömert, Z., Kocamaz, A. F., and Subha, V. (2018). Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment. Computers in biology and medicine, 99:85–97.
Eckmann, J.-P., Kamphorst, S. O., and Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhysics Letters, 4(9):973–977.
Faouzi, J. (2017-2021). pyts.image.recurrenceplot - pyts 0.13.0 documentation. [link]. Acesso em: 07 de mar. de 2025.
Goshvarpour, A., Goshvarpour, A., and Rahati, S. (2011). Analysis of lagged poincare plots in heart rate signals during meditation. Digital Signal Processing, 21(2):208–214.
Gunaratne, S. A., Panditharatne, S. D., and Chandraharan, E. (2022). Prediction of neonatal acidosis based on the type of fetal hypoxia observed on the cardiotocograph (ctg). European Journal of Medical and Health Sciences, 4(2):8–18.
Kanagal, D. V. and Praveen, B. (2022). Intrapartum fetal monitoring and its correlation with umbilical cord blood ph and early neonatal outcome: A prospective cohort study. Journal of South Asian Federation of Obstetrics and Gynaecology, 14(1):63–67.
Li, J., Chen, Z.-Z., Huang, L., Fang, M., Li, B., Fu, X., Wang, H., and Zhao, Q. (2018). Automatic classification of fetal heart rate based on convolutional neural network. IEEE Internet of Things Journal, 6(2):1394–1401.
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12):6999–7019.
Liang, S. and Li, Q. (2021). Automatic evaluation of fetal heart rate based on deep learning. In 2021 2nd information communication technologies conference (ICTC), pages 235–240. IEEE.
Muccini, A. M., Tran, N. T., Hale, N., McKenzie, M., Snow, R. J., Walker, D. W., and Ellery, S. J. (2022). The effects of in utero fetal hypoxia and creatine treatment on mitochondrial function in the late gestation fetal sheep brain. Oxidative Medicine and Cellular Longevity, 2022(1):3255296.
Ponsiglione, A. M., Cosentino, C., Cesarelli, G., Amato, F., and Romano, M. (2021). A comprehensive review of techniques for processing and analyzing fetal heart rate signals. Sensors, 21(18):6136.
Romano, M., Faiella, G., Bifulco, P., D’Addio, G., Clemente, F., and Cesarelli, M. (2014). Outliers detection and processing in ctg monitoring (2014) xiii mediterranean conference on medical and biological engineering and computing 2013.
Salahuddin, Z., Woodruff, H. C., Chatterjee, A., and Lambin, P. (2022). Transparency of deep neural networks for medical image analysis: A review of interpretability methods. Computers in biology and medicine, 140:105111.
Silva, B., Ribeiro, M., and Henriques, T. S. (2022). Compression of different time series representations in asphyxia detection. In 2022 E-Health and Bioengineering Conference (EHB), pages 1–5. IEEE.
Xiao, Y., Lu, Y., Liu, M., Zeng, R., and Bai, J. (2022). A deep feature fusion network for fetal state assessment. Frontiers in Physiology, 13:969052.
Zhao, Z., Zhang, Y., Comert, Z., and Deng, Y. (2019). Computer-aided diagnosis system of fetal hypoxia incorporating recurrence plot with convolutional neural network. Frontiers in physiology, 10:255.
Publicado
09/06/2025
Como Citar
COIMBRA, André R.; RIBEIRO, Maria; REBELO, Ana Cristina Silva; OLIVEIRA-JR, Antonio.
Detecção de Hipóxia Fetal explorando Representações de Séries Temporais com Redes Neurais Convolucionais. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 919-930.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7873.
