ECG-ResNeXt: Age Prediction in Pediatric Electrocardiograms and Its Correlations with Comorbidities
Resumo
Doenças cardiovasculares são a principal causa de mortalidade global, sendo o eletrocardiograma (ECG) essencial para avaliar a saúde cardíaca. Além do diagnóstico automático, o uso de modelos de inteligência artificial para a predição de idade a partir do ECG tem demonstrado grande potencial, de modo que quadros clínicos adversos sejam identificados por erros na idade predita. Neste trabalho pioneiro, exploramos a predição de idade em dados pediátricos, propondo o modelo ECG-ResNeXt, que incorpora avanços como inverted bottlenecks e Global Response Normalization e superou resultados obtidos por modelos anteriores. Além disso, analisamos correlações entre erros de predição e comorbidades, ressaltando o potencial clínico deste estudo.
Palavras-chave:
Predição de Idade, Redes Neurais, Eletrocardiogramas Pediátricos
Referências
Chan, T. C., Sharieff, G. Q., and Brady, W. J. (2008). Electrocardiographic manifestations: pediatric ecg. The Journal of emergency medicine, 35(4):421–430.
Chang, C.-H., Lin, C.-S., Luo, Y.-S., Lee, Y.-T., and Lin, C. (2022). Electrocardiogram-based heart age estimation by a deep learning model provides more information on the incidence of cardiovascular disorders. Frontiers in Cardiovascular Medicine, 9:754909.
Che, C., Zhang, P., Zhu, M., Qu, Y., and Jin, B. (2021). Constrained transformer network for ecg signal processing and arrhythmia classification. BMC Medical Informatics and Decision Making, 21(1):184.
Chen, J., Huang, S., Zhang, Y., Chang, Q., Zhang, Y., Li, D., Qiu, J., Hu, L., Peng, X., Du, Y., et al. (2024). Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts. Nature Communications, 15(1):976.
Ebrahimi, Z., Loni, M., Daneshtalab, M., and Gharehbaghi, A. (2020). A review on deep learning methods for ecg arrhythmia classification. Expert Systems with Applications: X, 7:100033.
García-Vicente, C., Gutiérrez-Tobal, G. C., Jiménez-García, J., Martín-Montero, A., Gozal, D., and Hornero, R. (2023). Ecg-based convolutional neural network in pediatric obstructive sleep apnea diagnosis. Computers in Biology and Medicine, 167:107628.
Hirota, N., Suzuki, S., Motogi, J., Nakai, H., Matsuzawa, W., Takayanagi, T., Umemoto, T., Hyodo, A., Satoh, K., Arita, T., et al. (2023). Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms. IJC Heart & Vasculature, 44:101172.
Hu, R., Chen, J., and Zhou, L. (2022). A transformer-based deep neural network for arrhythmia detection using continuous ecg signals. Computers in Biology and Medicine, 144:105325.
Lima, E. M., Ribeiro, A. H., Paixão, G. M., Ribeiro, M. H., Pinto-Filho, M. M., Gomes, P. R., Oliveira, D. M., Sabino, E. C., Duncan, B. B., Giatti, L., et al. (2021). Deep neural network-estimated electrocardiographic age as a mortality predictor. Nature communications, 12(1):5117.
Liu, X., Wang, H., Li, Z., and Qin, L. (2021). Deep learning in ecg diagnosis: A review. Knowledge-Based Systems, 227:107187.
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976–11986.
Loshchilov, I. and Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983.
Loshchilov, I. and Hutter, F. (2017). Decoupled weight decay regularization. arXiv pre-print arXiv:1711.05101.
Mayourian, J., Geggel, R., La Cava, W. G., Ghelani, S. J., and Triedman, J. K. (2024). Pediatric electrocardiogram-based deep learning to predict secundum atrial septal defects. Pediatric Cardiology, pages 1–6.
Mori, H., Inai, K., Sugiyama, H., and Muragaki, Y. (2021). Diagnosing atrial septal defect from electrocardiogram with deep learning. Pediatric cardiology, 42(6):1379–1387.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520.
Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I. S., and Xie, S. (2023). Conv-next v2: Co-designing and scaling convnets with masked autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16133–16142.
World Health Organization (2024). Cardiovascular diseases. Accessed: 2024-08-16.
Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., and Veloso, M. (2023). Financial time series forecasting using cnn and transformer. arXiv preprint arXiv:2304.04912.
Chang, C.-H., Lin, C.-S., Luo, Y.-S., Lee, Y.-T., and Lin, C. (2022). Electrocardiogram-based heart age estimation by a deep learning model provides more information on the incidence of cardiovascular disorders. Frontiers in Cardiovascular Medicine, 9:754909.
Che, C., Zhang, P., Zhu, M., Qu, Y., and Jin, B. (2021). Constrained transformer network for ecg signal processing and arrhythmia classification. BMC Medical Informatics and Decision Making, 21(1):184.
Chen, J., Huang, S., Zhang, Y., Chang, Q., Zhang, Y., Li, D., Qiu, J., Hu, L., Peng, X., Du, Y., et al. (2024). Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts. Nature Communications, 15(1):976.
Ebrahimi, Z., Loni, M., Daneshtalab, M., and Gharehbaghi, A. (2020). A review on deep learning methods for ecg arrhythmia classification. Expert Systems with Applications: X, 7:100033.
García-Vicente, C., Gutiérrez-Tobal, G. C., Jiménez-García, J., Martín-Montero, A., Gozal, D., and Hornero, R. (2023). Ecg-based convolutional neural network in pediatric obstructive sleep apnea diagnosis. Computers in Biology and Medicine, 167:107628.
Hirota, N., Suzuki, S., Motogi, J., Nakai, H., Matsuzawa, W., Takayanagi, T., Umemoto, T., Hyodo, A., Satoh, K., Arita, T., et al. (2023). Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms. IJC Heart & Vasculature, 44:101172.
Hu, R., Chen, J., and Zhou, L. (2022). A transformer-based deep neural network for arrhythmia detection using continuous ecg signals. Computers in Biology and Medicine, 144:105325.
Lima, E. M., Ribeiro, A. H., Paixão, G. M., Ribeiro, M. H., Pinto-Filho, M. M., Gomes, P. R., Oliveira, D. M., Sabino, E. C., Duncan, B. B., Giatti, L., et al. (2021). Deep neural network-estimated electrocardiographic age as a mortality predictor. Nature communications, 12(1):5117.
Liu, X., Wang, H., Li, Z., and Qin, L. (2021). Deep learning in ecg diagnosis: A review. Knowledge-Based Systems, 227:107187.
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976–11986.
Loshchilov, I. and Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983.
Loshchilov, I. and Hutter, F. (2017). Decoupled weight decay regularization. arXiv pre-print arXiv:1711.05101.
Mayourian, J., Geggel, R., La Cava, W. G., Ghelani, S. J., and Triedman, J. K. (2024). Pediatric electrocardiogram-based deep learning to predict secundum atrial septal defects. Pediatric Cardiology, pages 1–6.
Mori, H., Inai, K., Sugiyama, H., and Muragaki, Y. (2021). Diagnosing atrial septal defect from electrocardiogram with deep learning. Pediatric cardiology, 42(6):1379–1387.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520.
Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I. S., and Xie, S. (2023). Conv-next v2: Co-designing and scaling convnets with masked autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16133–16142.
World Health Organization (2024). Cardiovascular diseases. Accessed: 2024-08-16.
Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., and Veloso, M. (2023). Financial time series forecasting using cnn and transformer. arXiv preprint arXiv:2304.04912.
Publicado
17/11/2024
Como Citar
DUTENHEFNER, Pedro Robles et al.
ECG-ResNeXt: Age Prediction in Pediatric Electrocardiograms and Its Correlations with Comorbidities. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 49-60.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245277.
