Skip to main content
SpringerLink
Log in

ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13376))

Included in the following conference series:

Abstract

Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99% of overall accuracy for each register while keeping the computational cost low.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Radha, R., Shahzadi, S.K., Al-Sayah, M.H.: Fluorescent immunoassays for detection and quantification of cardiac troponin I: a short review. Molecules. 26(16), 4812 (2021). https://doi.org/10.3390/molecules26164812

  2. Wongthida, T., Lumkul, L., Patumanond, J., Wongtheptian, W., Piyayotai, D., Phinyo, P.: Development of a clinical risk score for prediction of life-threatening arrhythmia events in patients with ST elevated acute coronary syndrome after primary percutaneous coronary intervention. Int. J. Environ. Res. Public Health 19(4), 1997 (2022). https://doi.org/10.3390/ijerph19041997

    Article  Google Scholar 

  3. Kelley, R.E., Kelley, B.P.: Heart-brain relationship in stroke. Biomedicines. 9(12), 1835 (2021). https://doi.org/10.3390/biomedicines9121835

  4. Shaghiera, A.D., Widiyanti, P., Yusuf, H.: Synthesis and characterization of injectable hydrogels with varying collagen-chitosan-thymosin \(\beta \)4 composition for myocardial infarction therapy. J. Func. Biomater. 9(2), 33 (2018). https://doi.org/10.3390/jfb9020033

  5. Patel, P., et al.: Improved blood pressure control to reduce cardiovascular disease morbidity and mortality: the standardized hypertension treatment and prevention project. J. Clin. Hypertens. 18(12), 1284–1294 (2016). https://doi.org/10.1111/jch.12861

    Article  Google Scholar 

  6. Sannino, G., de Pietro, G.: A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Future Gener. Comput. Syst. 86, 446–455 (2018). https://doi.org/10.1016/j.future.2018.03.057

    Article  Google Scholar 

  7. Vizitiu, A., Nita, C.I., Toev, R.M., Suditu, T., Suciu, C., Itu, L.M.: framework for privacy-preserving wearable health data analysis: proof-of-concept study for atrial fibrillation detection. Appl. Sci. 11(19), 9049 (2021). https://doi.org/10.3390/app11199049

  8. Sraitih, M., Jabrane, Y., el Hassani, A.H.: An automated system for ECG arrhythmia detection using machine learning techniques. J. Clin. Med. 10(22), 5450 (2021). https://doi.org/10.3390/jcm10225450

  9. Vargas-muñoz, A.M., Chamorro-sangoquiza, D.C., Umaquinga-criollo, A.C.: Diseño de un prototipo de bajo coste computacional para detección de arritmias cardiacas, pp. 470–480 (2020)

    Google Scholar 

  10. Rodríguez-Sotelo, J.L., Peluffo-Ordoñez, D., Cuesta-Frau, D., Castellanos-Domínguez, G.: Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering. Comput. Methods Programs Biomed. 108(1), 250–261 (2012). https://doi.org/10.1016/J.CMPB.2012.04.007

    Article  Google Scholar 

  11. Sansone, M., Fusco, R., Pepino, A., Sansone, C: Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review (2013). http://www.sciencedirect.com/

  12. Tripathy, S., Singh, R.: Convolutional neural network: an overview and application in image classification. In: Poonia, R.C., Singh, V., Singh Jat, D., Diván, M.J., Khan, M.S. (eds.) Proceedings of Third International Conference on Sustainable Computing. AISC, vol. 1404, pp. 145–153. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-4538-9_15

    Chapter  Google Scholar 

  13. Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C, Xu, W.: CNN-RNN: a unified framework for multi-label image classification (2016)

    Google Scholar 

  14. Ghorbanian, P., Ghaffari, A., Jalali, A., Nataraj, C.: Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier. Comput. Cardiol. 2010, 669–672 (2010)

    Google Scholar 

  15. Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001). https://doi.org/10.1109/51.932724

    Article  Google Scholar 

  16. Ramkumar, M, Ganesh Babu, C., Vinoth Kumar, K., Hepsiba, D., Manjunathan, A., Sarath Kumar, R.: ECG cardiac arrhythmias classification using DWT, ICA and MLP neural networks. J. Phys. Conf. Ser. 1831(1), 012015 (2021). https://doi.org/10.1088/1742-6596/1831/1/012015

  17. Sheta, A., et al.: Diagnosis of obstructive sleep apnea from ECG signals using machine learning and deep learning classifiers. Appl. Sci. 11(14), 6622 (2021). https://doi.org/10.3390/app11146622

    Article  Google Scholar 

  18. Kiranyaz, S., Ince, T., Abdeljaber, O., Avci, O., Gabbouj, M.: 1-D convolutional neural networks for signal processing applications. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, May 2019, vol. 2019, pp. 8360–8364 (2019). https://doi.org/10.1109/ICASSP.2019.8682194

  19. Zhai, X., Tin, C.: Automated ECG classification using dual heartbeat coupling based on convolutional neural network. IEEE Access 6, 27465–27472 (2018). https://doi.org/10.1109/ACCESS.2018.2833841

    Article  Google Scholar 

  20. Rangappa, V.G., Prasad, S.V.A.V., Agarwal, A.: Classification of cardiac arrhythmia stages using hybrid features extraction with K-nearest neighbour classifier of ECG signals. Int. J. Intell. Eng. Syst. 11(6), 21–32 (2018). https://doi.org/10.22266/IJIES2018.1231.03

    Article  Google Scholar 

  21. Nanjundegowda, R., Meshram, V.A.: Arrhythmia detection based on hybrid features of T-wave in Electrocardiogram. Int. J. Intell. Eng. Syst. 11(1), 153–162 (2018). https://doi.org/10.22266/ijies2018.0228.16

    Article  Google Scholar 

  22. Scirè, A, Tropeano, F., Anagnostopoulos, A., Chatzigiannakis, I.: Fog-computing-based heartbeat detection and arrhythmia classification using machine learning. Algorithms. 12(2), 32 (2019). https://doi.org/10.3390/a12020032

  23. Luz, E.J.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: ECG-based heartbeat classification for arrhythmia detection: a survey. Comput. Methods Progr. Biomed. 127, 144–164 (2016). https://doi.org/10.1016/j.cmpb.2015.12.008

    Article  Google Scholar 

  24. Patro, K.K., Kumar, P.R.: De-noising of ECG raw signal by cascaded window based digital filters configuration. In: 2015 IEEE Power, Communication and Information Technology Conference, PCITC 2015 - Proceedings, March 2016, pp. 120–124 (2016). https://doi.org/10.1109/PCITC.2015.7438145

  25. Blanco-Velasco, M., Weng, B., Barner, K.E.: ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38(1), 1–13 (2008). https://doi.org/10.1016/j.compbiomed.2007.06.003

    Article  Google Scholar 

  26. Pinto, J.R., Cardoso, J.S., Lourenço, A., Carreiras, C.: Towards a continuous biometric system based on ECG signals acquired on the steering wheel. Sensors 17(10), 2228 (2017). https://doi.org/10.3390/s17102228

    Article  Google Scholar 

  27. Lourenço, A., Silva, H., Leite, P., Lourenço, R., Fred, A.: Real time electrocardiogram segmentation for finger based ECG biometrics. In: BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, pp. 49–54 (2012). https://doi.org/10.5220/0003777300490054

  28. Ali, M.: PyCaret: an open source, low-code machine learning library in Python, PyCaret version 1.0.0, April 2020. https://www.pycaret.org

  29. Oweis, R., Oweis, R.J., Al-Tabbaa, B.O.: QRS detection and heart rate variability analysis: a survey. Biomed. Sci. Eng. 2(1), 13–34 (2014). https://doi.org/10.12691/bse-2-1-3

    Article  Google Scholar 

  30. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  31. Chollet, F.: Keras: the python deep learning library. Astrophys. Source Code Libr. ASCL-1806 (2018)

    Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the valuable support given by the SDAS Research Group (https://sdas-group.com/).

Author information

Authors and Affiliations

  1. School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, 100119, Ecuador

    Eduardo Cepeda & Diego Almeida-Galárraga

  2. Smart Data Analysis Systems Group (SDAS Research Group), 47963, Ben Guerir, Morocco

    Nadia N. Sánchez-Pozo & Juan González-Vergara

  3. Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150, Ben Guerir, Morocco

    Diego H. Peluffo-Ordóñez

Authors
  1. Eduardo Cepeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadia N. Sánchez-Pozo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diego H. Peluffo-Ordóñez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan González-Vergara
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diego Almeida-Galárraga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eduardo Cepeda .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cepeda, E., Sánchez-Pozo, N.N., Peluffo-Ordóñez, D.H., González-Vergara, J., Almeida-Galárraga, D. (2022). ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13376. Springer, Cham. https://doi.org/10.1007/978-3-031-10450-3_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10450-3_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10449-7

  • Online ISBN: 978-3-031-10450-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation