Um Método Ensemble para Classificação de Arritmias: Uma Avaliação Com Mais de 10 Mil Registros de Sinais de ECG
Resumo
O diagnóstico precoce de arritmias é de fundamental importância, sendo uma das doenças cardíacas que causa maior número de mortes no planeta, segundo a OMS. Por isso, a automação do processo de identificação de arritmia é desejável. Nesse contexto, um modelo de classificação automática de arritmias em ECGs é proposto baseado em esquema de votação (voting ensemble) e Discrete Wavelet Transform (DWT). Avaliado em um conjunto de dados com mais de 10 mil pacientes e sob o paradigma inter-patient, o modelo proposto alcançou F1-score médio de 0,93, um aumento em eficiência de 2,15% em relação ao Random Forest e 1,07% em relação ao GradientBoost e ao XGradient Boost. Com isso, o modelo proposto apresenta grande potencial para uso real devido sua robustez e poder de generalização.
Referências
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Dias, F. M., Monteiro, H. L., Cabral, T. W., Naji, R., Kuehni, M., and Luz, E. J. d. S. (2021). Arrhythmia classification from single-lead ecg signals using the inter-patient paradigm. Computer Methods and Programs in Biomedicine, 202:105948.
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Osowski, S., Markiewicz, T., and Hoai, L. T. (2008). Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement, 41(6):610-617.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.
Poungponsri, S. and Yu, X.-H. (2013). An adaptive filtering approach for electrocardiogram (ecg) signal noise reduction using neural networks. Neurocomputing, 117:206-213.
Rakshit, M. and Das, S. (2018). An efficient ecg denoising methodology using empirical mode decomposition and adaptive switching mean filter. Biomedical signal processing and control, 40:140-148.
Rodan, A. and Tino, P. (2010). Minimum complexity echo state network. IEEE transactions on neural networks, 22(1):131-144.
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.
Silveira, A. C. d., Sobrinho, Á., Silva, L. D. d., Costa, E. d. B., Pinheiro, M. E., and Perkusich, A. (2022). Exploring early prediction of chronic kidney disease using machine learning algorithms for small and imbalanced datasets. Applied Sciences, 12(7):3673.
Sternickel, K. (2002). Automatic pattern recognition in ecg time series. Computer methods and programs in biomedicine, 68(2):109-115.
Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media, 2nd edition.
Wang, L., Sun, W., Chen, Y., Li, P., and Zhao, L. (2018). 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, 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., Wan, F., Wong, C. M., and Zhang, L. (2016). Adaptive fourier decomposition based ecg denoising. Computers in Biology and Medicine, 77:195-205.
WHO (2021). Cardiovascular diseases (cvds). Disponível em: [link].
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.
Zhao, Q. and Zhang, L. (2005). Ecg feature extraction and classification using wavelet transform and support vector machines. In 2005 International Conference on Neural Networks and Brain, volume 2, pages 1089-1092. IEEE.
Zheng, J., Zhang, J., Danioko, S., Yao, H., Guo, H., and Rakovski, C. (2020). A 12lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Scientific Data, 7(1):1-8.
Dias, F. M., Monteiro, H. L., Cabral, T. W., Naji, R., Kuehni, M., and Luz, E. J. d. S. (2021). Arrhythmia classification from single-lead ecg signals using the inter-patient paradigm. Computer Methods and Programs in Biomedicine, 202:105948.
Garcia, G., Moreira, G., Menotti, D., and Luz, E. (2017). Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports, 7(1):1-11.
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.
Kadambe, S., Murray, R., and Boudreaux-Bartels, G. F. (1999). Wavelet transform-based qrs complex detector. IEEE Transactions on biomedical Engineering, 46(7):838-848.
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.
Mousavi, S. and Afghah, F. (2019). Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1308-1312. IEEE.
Osowski, S., Markiewicz, T., and Hoai, L. T. (2008). Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement, 41(6):610-617.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.
Poungponsri, S. and Yu, X.-H. (2013). An adaptive filtering approach for electrocardiogram (ecg) signal noise reduction using neural networks. Neurocomputing, 117:206-213.
Rakshit, M. and Das, S. (2018). An efficient ecg denoising methodology using empirical mode decomposition and adaptive switching mean filter. Biomedical signal processing and control, 40:140-148.
Rodan, A. and Tino, P. (2010). Minimum complexity echo state network. IEEE transactions on neural networks, 22(1):131-144.
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.
Silveira, A. C. d., Sobrinho, Á., Silva, L. D. d., Costa, E. d. B., Pinheiro, M. E., and Perkusich, A. (2022). Exploring early prediction of chronic kidney disease using machine learning algorithms for small and imbalanced datasets. Applied Sciences, 12(7):3673.
Sternickel, K. (2002). Automatic pattern recognition in ecg time series. Computer methods and programs in biomedicine, 68(2):109-115.
Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media, 2nd edition.
Wang, L., Sun, W., Chen, Y., Li, P., and Zhao, L. (2018). 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, 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., Wan, F., Wong, C. M., and Zhang, L. (2016). Adaptive fourier decomposition based ecg denoising. Computers in Biology and Medicine, 77:195-205.
WHO (2021). Cardiovascular diseases (cvds). Disponível em: [link].
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.
Zhao, Q. and Zhang, L. (2005). Ecg feature extraction and classification using wavelet transform and support vector machines. In 2005 International Conference on Neural Networks and Brain, volume 2, pages 1089-1092. IEEE.
Zheng, J., Zhang, J., Danioko, S., Yao, H., Guo, H., and Rakovski, C. (2020). A 12lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Scientific Data, 7(1):1-8.
Publicado
07/06/2022
Como Citar
OLIVEIRA, Rafael F.; FERREIRA, Anderson A.; MOREIRA, Gladston J. P.; LUZ, Eduardo J. S..
Um Método Ensemble para Classificação de Arritmias: Uma Avaliação Com Mais de 10 Mil Registros de Sinais de ECG. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 13-24.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222429.
