ECG Arrhythmia Classification Using Recurrence Plot and ResNet-18


  • Joshua Gutierrez-Ojeda
  • Volodymyr Ponomaryov
  • Jose-Agustin Almaraz-Damian
  • Rogelio Reyes-Reyes
  • Clara Cruz-Ramos



Electrocardiogram (ECG), MIT-BIH arrhythmia database, Recurrence Plot (RP), ResNet-18, Random Under Sampling, ROC-AUC, Deep Learning


Cardiovascular diseases are the leading cause of death worldwide, claiming approximately

17.9 million lives each year. In this study, a novel CAD system to detect and classify electrocardiogram (ECG) signals is presented. Designed system employs the recurrence plot (RP) approach that transforms a ECG signal into a 2D representative colour image, finally performing their classifications via employment of Deep Learning architecture (ResNet-18). Novel system includes two steps, where the first step is the preprocessing one, which performs segmentation of the data into two-second intervals, finally forming images via the RP approach; following, in the second step, the RP images are classified by the ResNet- 18 network. The proposed method is evaluated on the MIT-BIH arrhythmia database where 5 principal types of arrhythmias that have medical relevance should be classified. Novel system can classify the before-mentioned quantity of diseases according to the AAMI Standard and appears to demonstrate good performance in terms of criteria: overall accuracy of 97.62%, precision of 95.42%, recall of 95.42%, F1-Score of 95.06%, and AUC of 95.7% that are competitive with better state-of-the-art systems. Additionally. the method demonstrated the ability in mitigating the problem of imbalanced samples.


D. Vélez Rodríguez, ECG electrocardiogram, Marbán Libros, 2013.

“OMS The Burden of Cardiovascular Disease,” May 2019, "Accessed on October. 21, 2021". [Online]. Available:

J. Pan and W. J. Tompkins, “A real-time qrs detection algorithm,” IEEE transactions on biomedical engineering, no. 3, pp. 230–236, 1985.

H.-Y. Lin, S.-Y. Liang, Y.-L. Ho, Y.-H. Lin, and H.-P. Ma, “Discrete- wavelet-transform-based noise removal and feature extraction for ECG signals,” IRBM, vol. 35, no. 6, pp. 351–361, 2014, Healthcom 2013.

S. K. Berkaya, A. K. Uysal, E. S. Gunal, S. Ergin, S. Gunal, and M. B. Gulmezoglu, “A survey on ECG analysis,” Biomedical Signal Processing and Control, vol. 43, pp. 216–235, 2018.

G. C. Jana, A. Agrawal, P. K. Pattnaik, and M. Sain, “Dwt-emd feature level fusion based approach over multi and single channel eeg signals for seizure detection,” Diagnostics, vol. 12, no. 2, p. 324, 2022.

J. Han, G. Sun, X. Song, J. Zhao, J. Zhang, and Y. Mao, “Detecting ecg abnormalities using an ensemble framework enhanced by bayesian belief network,” Biomedical Signal Processing and Control, vol. 72, p. 103320, 2022.

H. Wang, Z. Lei, X. Zhang, B. Zhou, and J. Peng, “Machine learning basics,” Deep learning, pp. 98–164, 2016.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.

O. Ozaltin and O. Yeniay, “A novel proposed cnn–svm architecture for ECG scalograms classification,” Soft Computing, pp. 1–20, 2022.

Z. Ahmad, A. Tabassum, L. Guan, and N. M. Khan, “ECG heartbeat classification using multimodal fusion,” IEEE Access, vol. 9, pp. 100 615–100 626, 2021.

H. Zhang, C. Liu, Z. Zhang, Y. Xing, X. Liu, R. Dong, Y. He, L. Xia, and F. Liu, “Recurrence plot-based approach for cardiac arrhythmia classification using inception-resnet-v2,” Frontiers in physiology, vol. 12, p. 648950, 2021.

B. M. Mathunjwa, Y.-T. Lin, C.-H. Lin, M. F. Abbod, M. Sadrawi, and J.-S. Shieh, “ECG recurrence plot-based arrhythmia classification using two-dimensional deep residual CNN features,” Sensors, vol. 22, no. 4, p. 1660, 2022.

N. P. Martono, T. Nishiguchi, and H. Ohwada, “ECG signal classification using recurrence plot-based approach and deep learning for arrhythmia prediction,” Proceedings of the 14th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28–30, 2022, Proceedings, Part I. Springer, 2022, pp. 327–335.

“Mit-bih arrhythmia database,” February 2005, "Accessed on November. 21, 2021". [Online]. Available at:

N. Association for the Advancement of Medical Instrumentation et al., “Testing and reporting performance results of cardiac rhythm and st segment measurement algorithms,” ANSI/AAMI EC38, vol. 1998, p. 46, 1998.

G. B. Moody and R. G. Mark, “The impact of the mit-bih arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001.

J.-P. Eckmann, S. O. Kamphorst, D. Ruelle et al., “Recurrence plots of dynamical systems,” World Scientific Series on Nonlinear Science Series A, vol. 16, pp. 441–446, 1995.

N. Hatami, Y. Gavet, and J. Debayle, “Classification of time-series images using deep convolutional neural networks,” Proceedings of the Tenth International Conference on Machine Vision (ICMV 2017), vol. 10696. SPIE, 2018, pp. 242–249.

F. A. Faria, J. Almeida, B. Alberton, L. P. C. Morellato, and R. d. S. Torres, “Fusion of time series representations for plant recognition in phenology studies,” Pattern Recognition Letters, vol. 83, pp. 205–214, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” Proceedings of the 14th European Conference on Computer Vision–ECCV’2016: Amsterdam, The Netherlands, October 11–14, 2016, Part IV. Springer, 2016, pp. 630–645.

F. K. Chollet, “Keras,” 2015, "Accessed on November. 21, 2021". [Online]. Available:

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Pas- sos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.

G. Lemaître, F. Nogueira, and C. K. Aridas, “Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning,” Journal of Machine Learning Research, vol. 18, no. 17, pp. 1–5, 2017. [Online]. Available at:

S. Yang and G. Berdine, “The receiver operating characteristic (roc) curve,” The Southwest Respiratory and Critical Care Chronicles, vol. 5, no. 19, pp. 34–36, 2017.

R. Fayzrakhmanov, A. Kulikov, and P. Repp, “The difference between precision-recall and roc curves for evaluating the performance of credit card fraud detection models,” Proceedings of International Conference on Applied Innovation in IT, vol. 6, no. 1. Anhalt University of Applied Sciences, 2018, pp. 17–22.

V. García, R. A. Mollineda, and J. S. Sánchez, “Index of balanced accuracy: A performance measure for skewed class distributions,” Proceedings off 4th Iberian Conference on the Pattern Recognition and Image Analysis: IbPRIA 2009, Póvoa de Varzim, Portugal, June 10-12, 2009, Springer, 2009, pp. 441–448.

J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” Journal of Big Data, vol. 6, no. 1, pp. 1–54, 2019.




How to Cite

Gutierrez-Ojeda, J., Ponomaryov, V., Almaraz-Damian, J.-A., Reyes-Reyes, R., & Cruz-Ramos, C. (2023). ECG Arrhythmia Classification Using Recurrence Plot and ResNet-18. International Journal of Computing, 22(2), 140-148.