Arquitectura Híbrida Interpretable basada en Wavelets para la Clasificación Multiclase de Latidos ECG
DOI:
https://doi.org/10.33414/rtyc.56.22-39.2026Palabras clave:
ECG, clasificación de arritmias cardíacas, transformada wavelet, aprendizaje profundo, transformer, redes neuronales profundasResumen
Se presenta una arquitectura híbrida de aprendizaje profundo para la clasificación multiclase de latidos electrocardiográficos (ECG) utilizando la base MIT-BIH Arrhythmia. El enfoque combina bloques convolucionales multiescala tipo Inception con convoluciones separables en profundidad (depthwise separable convolutions), codificadores Transformer unidimensionales (Transformer 1D), unidades recurrentes bidireccionales basadas en Gated Recurrent Units (BiGRU) y mecanismos de atención contextual. La representación de cada latido se enriquece mediante características estadísticas obtenidas a partir de transformadas wavelet discretas multiescala y mediante una rama temporal decimada derivada de la señal original. Para abordar el desbalance de clases se emplean estrategias de aumentación fisiológica controlada, Borderline-SMOTE y una función de pérdida focal ponderada. El modelo alcanzó 99,88 % de exactitud en entrenamiento y 98,97 % en validación, mostrando además elevada separabilidad en el espacio latente y coherencia interpretativa mediante análisis SHapley Additive exPlanations (SHAP). Los resultados sugieren que la integración de representaciones espectro-temporales explícitas con modelado profundo híbrido constituye una alternativa robusta, interpretable y computacionalmente eficiente para la clasificación automática de arritmias cardíacas.
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Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adam, M., Gertych, A., Tan, R. S. (2017). A deep convolutional neural network model to classify heartbeats. Computers in Biology and Medicine, 89, 389–396. https://doi.org/10.1016/j.compbiomed.2017.08.022
Addison, P. S. (2005). Wavelet transforms and the ECG: A review. Physiological Measurement, 26(5), R155–R199. https://doi.org/10.1088/0967-3334/26/5/R01
Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition, 1251-1258. https://doi.org/10.1109/CVPR.2017.195
Daubechies, I. (1992). Ten lectures on wavelets. Society for Industrial and Applied Mathematics (SIAM). https://doi.org/10.1137/1.9781611970104
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C. K., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215-e220. https://doi.org/10.1161/01.CIR.101.23.e215
Han, H., Wang, W. Y., & Mao, B. H. (2005). Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In: Huang, DS., Zhang, XP., Huang, GB. (eds). Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_91
He, X., Hu, C., Ma, K., Huang, J., & He, H. (2025). ECG-based arrhythmia classification using discrete wavelet transform and attention-enhanced CNN-BiGRU model. Physical and Engineering Sciences in Medicine, 48, 1995–2009. https://doi.org/10.1007/s13246-025-01639-6
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-Excitation Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. 7132–7141. https://doi.org/10.1109/CVPR.2018.00745
Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016). Deep networks with stochastic depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds). Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham. https://doi.org/10.1007/978-3-319-46493-0_39
Islam, M. S., Hasan, K. F., Sultana, S., Uddin, S., Lio’, P., Quinn, J. M. W., & Moni, M. A. (2023). HARDC: A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN. Neural Networks, 162, 271–287. https://doi.org/10.1016/j.neunet.2023.03.004
Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P. A., & Petitjean, F. (2020). InceptionTime: Finding AlexNet for time series classification. Data Mining and Knowledge Discovery, 34(6), 1936–1962. https://doi.org/10.1007/s10618-020-00710-y
Jun, T. J., Nguyen, H. M., Kang, D., Kim, D., Kim, Y.-H., & Kim, D. (2018). ECG arrhythmia classification using a 2-D convolutional neural network. arXiv preprint arXiv:1804.06812.
Kachuee, M., Fazeli, S., & Sarrafzadeh, M. (2018). ECG heartbeat classification: A deep transferable representation. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), 443–444. https://doi.org/10.1109/ICHI.2018.00092
Lin, T. -Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017). Focal loss for dense object detection. 2017 IEEE International Conference on Computer Vision, Venice, Italy. 2980-2988. https://doi.org/10.1109/ICCV.2017.324
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1705.07874
Mallat, S. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693. https://doi.org/10.1109/34.192463
Meng, L., Tan, W., Ma, J., Wang, R., Yin, X., & Zhang, Y. (2022). Enhancing dynamic ECG heartbeat classification with lightweight transformer model. Artificial Intelligence in Medicine, 124, 102236. https://doi.org/10.1016/j.artmed.2022.102236
Pasupa, K., Vatathanavaro, S., & Tungjitnob, S. (2020). Convolutional neural network based focal loss for class imbalance problem: A case study of canine red blood cells morphology classification. arXiv preprint arXiv:2001.03329.
Ramachandran, P., Zoph, B., & Le, Q. V. (2017). Searching for activation functions. arXiv preprint arXiv:1710.05941.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 1–9. https://doi.org/10.1109/CVPR.2015.7298594
van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605. https://www.jmlr.org/papers/v9/vandermaaten08a.html
World Health Organization. (2023). Cardiovascular diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Yu, S. -N., & Chen, Y. -H. (2007). Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters, 28(10), 1142–1150. https://doi.org/10.1016/j.patrec.2007.01.017
Zhao, Z. (2023). Transforming ECG Diagnosis: An In-depth Review of Transformer-based Deep Learning Models in Cardiovascular Disease Detection. arXiv preprint arXiv:2306.01249.
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Derechos de autor 2026 Cesar Christian Holote Larrosa, Carlos Marcelo Gomez, Daniel Turra
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
