Explorando Redes Neurais de Grafos para Classificação de Arritmias
Resumo
Arritmia cardíaca é uma condição de risco e seu diagnóstico precoce é de fundamental importância. Por isso, a automação do processo de identificação de arritmia é desejável. Neste contexto, um modelo de classificação automática de arritmias em sinais de eletrocardiograma (ECG) é proposto baseado em Graph Neural Network (GNN) e o batimento cardíaco representado por meio de um grafo via algoritmo Visibility Graph (VG). São avaliados três modelos de redes GNN: uma rede convolutional de grafos com duas camadas (GCN-2L), outra com três camadas (GCN-3L) e GraphSAGE (GraphSAGE-4L). Sob o paradigma interpatient, o modelo GraphSAGE-4L apresentou o melhor desempenho, obtendo F1-score médio de 0,86 para as classes N, S e V.
Referências
ANSI/AAMI (2008). Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008.
Cohen, A. (1986). Biomedical Signal Processing: Compression and automatic recognition. CRC Press, 1st edition.
De Chazal, P., O'Dwyer, M., and Reilly, R. B. (2004). Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering, 51(7):1196-1206.
Do, K., Tran, T., and Venkatesh, S. (2019). Graph transformation policy network for chemical reaction prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 750-760.
Fout, A. M. (2017). Protein interface prediction using graph convolutional networks. PhD thesis, Colorado State University.
Hamilton, W. L., Ying, R., and Leskovec, J. (2017). Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 1025-1035.
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine, 25(1):65.
Kipf, T. N. and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv:1609.02907.
Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., and Okuno, Y. (2020). kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics, 12(1):1-10.
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., and Nuno, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences, 105(13):4972-4975.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324.
Luz, E. J. d. S., Schwartz, W. R., Cámara-Chávez, G., and Menotti, D. (2016). Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine, 127:144-164.
Micheli, A. (2009). Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks, 20(3):498-511.
Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., and Ortega, M. (2019). Heartbeat classification fusing temporal and morphological information of ecgs via ensemble of classifiers. Biomedical Signal Processing and Control, 47:41-48.
Moody, G. B. and Mark, R. G. (1990). The mit-bih arrhythmia database on cd-rom and software for use with it. In [1990] Proceedings Computers in Cardiology, pages 185-188. IEEE.
Rajesh, K. N. and Dhuli, R. (2018). Classification of imbalanced ecg beats using resampling techniques and adaboost ensemble classifier. Biomedical Signal Processing and Control, 41:242-254.
Rajpurkar, P., Hannun, A. Y., Haghpanahi, M., Bourn, C., and Ng, A. Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv:1707.01836.
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1):61-80.
Shi, H., Wang, H., Huang, Y., Zhao, L., Qin, C., and Liu, C. (2019). A hierarchical method based on weighted extreme gradient boosting in ecg heartbeat classification. Computer methods and programs in biomedicine, 171:1-10.
Sperduti, A. and Starita, A. (1997). Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks, 8(3):714-735.
Wang, L., Sun, W., Chen, Y., Li, P., and Zhao, L. (2018a). Wavelet transform based ecg denoising using adaptive thresholding. In Proceedings of the 2018 7th International Conference on Bioinformatics and Biomedical Science, pages 35-40.
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T., He, T., Karypis, G., Li, J., and Zhang, Z. (2019). Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv:1909.01315.
Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., and Ou, C. (2021). Automatic ecg classification using continuous wavelet transform and convolutional neural network. Entropy, 23(1):119.
Wang, Z., Chen, T., Ren, J., Yu, W., Cheng, H., and Lin, L. (2018b). Deep reasoning with knowledge graph for social relationship understanding. arXiv:1807.00504.
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4-24.
Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2018). How powerful are graph neural networks? arXiv:1810.00826.
Yang, P., Wang, D., Zhao, W.-B., Fu, L.-H., Du, J.-L., and Su, H. (2021). Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification. Biomedical Signal Processing and Control, 63:102138.
Zheng, J., Zhang, J., Danioko, S., Yao, H., Guo, H., and Rakovski, C. (2020). A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Scientific Data, 7(1):1-8.
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1:57-81.
Cohen, A. (1986). Biomedical Signal Processing: Compression and automatic recognition. CRC Press, 1st edition.
De Chazal, P., O'Dwyer, M., and Reilly, R. B. (2004). Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering, 51(7):1196-1206.
Do, K., Tran, T., and Venkatesh, S. (2019). Graph transformation policy network for chemical reaction prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 750-760.
Fout, A. M. (2017). Protein interface prediction using graph convolutional networks. PhD thesis, Colorado State University.
Hamilton, W. L., Ying, R., and Leskovec, J. (2017). Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 1025-1035.
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine, 25(1):65.
Kipf, T. N. and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv:1609.02907.
Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., and Okuno, Y. (2020). kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics, 12(1):1-10.
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., and Nuno, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences, 105(13):4972-4975.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324.
Luz, E. J. d. S., Schwartz, W. R., Cámara-Chávez, G., and Menotti, D. (2016). Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine, 127:144-164.
Micheli, A. (2009). Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks, 20(3):498-511.
Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., and Ortega, M. (2019). Heartbeat classification fusing temporal and morphological information of ecgs via ensemble of classifiers. Biomedical Signal Processing and Control, 47:41-48.
Moody, G. B. and Mark, R. G. (1990). The mit-bih arrhythmia database on cd-rom and software for use with it. In [1990] Proceedings Computers in Cardiology, pages 185-188. IEEE.
Rajesh, K. N. and Dhuli, R. (2018). Classification of imbalanced ecg beats using resampling techniques and adaboost ensemble classifier. Biomedical Signal Processing and Control, 41:242-254.
Rajpurkar, P., Hannun, A. Y., Haghpanahi, M., Bourn, C., and Ng, A. Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv:1707.01836.
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1):61-80.
Shi, H., Wang, H., Huang, Y., Zhao, L., Qin, C., and Liu, C. (2019). A hierarchical method based on weighted extreme gradient boosting in ecg heartbeat classification. Computer methods and programs in biomedicine, 171:1-10.
Sperduti, A. and Starita, A. (1997). Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks, 8(3):714-735.
Wang, L., Sun, W., Chen, Y., Li, P., and Zhao, L. (2018a). Wavelet transform based ecg denoising using adaptive thresholding. In Proceedings of the 2018 7th International Conference on Bioinformatics and Biomedical Science, pages 35-40.
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T., He, T., Karypis, G., Li, J., and Zhang, Z. (2019). Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv:1909.01315.
Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., and Ou, C. (2021). Automatic ecg classification using continuous wavelet transform and convolutional neural network. Entropy, 23(1):119.
Wang, Z., Chen, T., Ren, J., Yu, W., Cheng, H., and Lin, L. (2018b). Deep reasoning with knowledge graph for social relationship understanding. arXiv:1807.00504.
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4-24.
Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2018). How powerful are graph neural networks? arXiv:1810.00826.
Yang, P., Wang, D., Zhao, W.-B., Fu, L.-H., Du, J.-L., and Su, H. (2021). Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification. Biomedical Signal Processing and Control, 63:102138.
Zheng, J., Zhang, J., Danioko, S., Yao, H., Guo, H., and Rakovski, C. (2020). A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Scientific Data, 7(1):1-8.
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1:57-81.
Publicado
07/06/2022
Como Citar
OLIVEIRA, Rafael F.; FREITAS, Vander L. S.; MOREIRA, Gladston J. P.; LUZ, Eduardo J. S..
Explorando Redes Neurais de Grafos para Classificação de Arritmias. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 178-189.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222510.
