Skip to main content
SpringerLink
Log in

Electrocardiogram Quality Assessment with Autoencoder

  • Conference paper
  • First Online:
Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12744))

Included in the following conference series:

Abstract

ECG recordings from wearable devices are affected with a relatively high amount of noise due to body motion and long time of the examination, which leads to many false alarms on ill-state detection and forces medical staff to spend more time on describing each recording. ECG quality assessment is hard due to impulse character of the signal and its high variability. In this paper we describe an anomaly detection algorithm based on the Autoencoder trained on good quality examples only. Once trained, this neural network reconstructs clean ECG signals more accurately than noisy examples, which allows to distinguish both classes. Presented method achieves a normalized F1 score of 93.34% on the test set extracted from public dataset of 2011 PhysioNet/Computing in Cardiology Challenge, outperforming the solution based on the best competition participants. In contrary to many state-of-the-art methods it can be applied even on short, single-channel ECG signals.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet. Circulation 101(23), e215–e220 (2000)

    Article  Google Scholar 

  2. Silva, I., Moody, G.B., Celi, L.: Improving the quality of ECGS collected using mobile phones: the PhysioNet/computing in cardiology challenge 2011. In: 2011 Computing in Cardiology, pp. 273–276, September 2011

    Google Scholar 

  3. Clifford, G.D., et al.: AF classification from a short single lead ECG recording: the PhysioNet/computing in cardiology challenge 2017. In: 2017 Computing in Cardiology (CinC), pp. 1–4 (2017)

    Google Scholar 

  4. Moody, G., Mark, R.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001)

    Article  Google Scholar 

  5. Clifford, G.D., Behar, J., Li, Q., Rezek, I.: Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiol. Meas. 33(9), 1419 (2012)

    Article  Google Scholar 

  6. Behar, J., Oster, J., Li, Q., Clifford, G.D.: ECG signal quality during arrhythmia and its application to false alarm reduction. IEEE Trans. Biomed. Eng. 60(6), 1660–1666 (2013)

    Article  Google Scholar 

  7. Liu, C., et al.: Signal quality assessment and lightweight QRS detection for wearable ECG SmartVest system. IEEE Internet Things J. 6(2), 1363–1374 (2019)

    Article  Google Scholar 

  8. Kuzilek, J., Huptych, M., Chudacek, V., Spilka, J., Lhotska, L.: Data driven approach to ECG signal quality assessment using multistep SVM classification. In: 2011 Computing in Cardiology, pp. 453–455, September 2011

    Google Scholar 

  9. Chudacek, V., Zach, L., Kuzilek, J., Spilka, J., Lhotska, L.: Simple scoring system for ECG quality assessment on android platform. In: 2011 Computing in Cardiology, pp. 449–451, September 2011

    Google Scholar 

  10. Hayn, D., Jammerbund, B., Schreier, G.: ECG quality assessment for patient empowerment in mHealth applications. In: 2011 Computing in Cardiology, pp. 353–356, September 2011

    Google Scholar 

  11. Liu, C., Li, P., Zhao, L., Liu, F., Wang, R.: Real-time signal quality assessment for ECGs collected using mobile phones. In: 2011 Computing in Cardiology, pp. 357–360, September 2011

    Google Scholar 

  12. Satija, U., Ramkumar, B., Manikandan, M.S.: A simple method for detection and classification of ECG noises for wearable ECG monitoring devices. In: 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 164–169, February 2015

    Google Scholar 

  13. Satija, U., Ramkumar, B., Manikandan, M.S.: Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J. Biomed. Health Inform. 22(3), 722–732 (2018)

    Article  Google Scholar 

  14. Satija, U., Ramkumar, B., Manikandan, M.S.: A new automated signal quality-aware ECG beat classification method for unsupervised ECG diagnosis environments. IEEE Sens. J. 19(1), 277–286 (2019)

    Article  Google Scholar 

  15. Hermawan, I.,et al.: Temporal feature and heuristics-based noise detection over classical machine learning for ECG signal quality assessment. In: 2019 International Workshop on Big Data and Information Security (IWBIS), pp. 1–8, October 2019

    Google Scholar 

  16. Redmond, S.J., Xie, Y., Chang, D., Basilakis, J., Lovell, N.H.: Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiol. Meas. 33(9), 1517 (2012)

    Article  Google Scholar 

  17. Moeyersons, J., Testelmans, D., Buyse, B., Willems, R., Van Huffel, S., Varon, C.: Evaluation of a continuous ECG quality indicator based on the autocorrelation function. In: 2018 Computing in Cardiology Conference (CinC), vol. 45, pp. 1–4, September 2018

    Google Scholar 

  18. Martinez-Tabares, F.J., Espinosa-Oviedo, J., Castellanos-Dominguez, G.: Improvement of ecg signal quality measurement using correlation and diversity-based approaches. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4295–4298, August 2012

    Google Scholar 

  19. Morgado, E., et al.: Quality estimation of the electrocardiogram using cross-correlation among leads. BioMed. Eng. OnLine 14, 59 (2015)

    Google Scholar 

  20. Naseri, H., Homaeinezhad, M.: Electrocardiogram signal quality assessment using an artificially reconstructed target lead. Comput. Meth. Biomech. Biomed. Eng. 18(10), 1126–1141 (2015), pMID: 24460414

    Google Scholar 

  21. Shahriari, Y., Fidler, R., Pelter, M.M., Bai, Y., Villaroman, A., Hu, X.: Electrocardiogram signal quality assessment based on structural image similarity metric. IEEE Trans. Biomed. Eng. 65(4), 745–753 (2018)

    Article  Google Scholar 

  22. Shi, Y., et al.: Robust assessment of ECG signal quality for wearable devices. In: 2019 IEEE International Conference on Healthcare Informatics (ICHI), pp. 1–3, June 2019

    Google Scholar 

  23. Castiglioni, P., Meriggi, P., Faini, A., Rienzo, M.D.: Cepstral based approach for online quantification of ECG quality in freely moving subjects. In: 2011 Computing in Cardiology, pp. 625–628, September 2011

    Google Scholar 

  24. Zhou, X., Zhu, X., Nakamura, K., Mahito, N.: ECG quality assessment using 1D-convolutional neural network. In: 2018 14th IEEE International Conference on Signal Processing (ICSP), pp. 780–784, August 2018

    Google Scholar 

  25. Ghosal, P., Sarkar, D., Kundu, S., Roy, S., Sinha, A., Ganguli, S.: ECG beat quality assessment using self organizing map. In: 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix), pp. 1–5, November 2017

    Google Scholar 

  26. Karpinski, M., Khoma, V., Dudvkevych, V., Khoma, Y., Sabodashko, D.: Autoencoder neural networks for outlier correction in ECG- based biometric identification. In: 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), pp. 210–215 (2018)

    Google Scholar 

  27. Bellinger, C., Drummond, C., Japkowicz, N.: Beyond the boundaries of SMOTE. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 248–263. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46128-1_16

    Chapter  Google Scholar 

  28. Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. (JAIR) 16, 321–357 (2002)

    Article  Google Scholar 

  29. Sharma, S., Bellinger, C., Japkowicz, N., Berg, R., Ungar, K.: Anomaly detection in gamma ray spectra: a machine learning perspective. In: 2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications, pp. 1–8, July 2012

    Google Scholar 

  30. Lecun, Y.: PhD thesis: Modeles connexionnistes de l’apprentissage (connectionist learning models). Universite P. et M. Curie (Paris 6) (6 1987)

    Google Scholar 

  31. Bourlard, H., Kamp, Y.: Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern. 59(4), 291–294 (1988)

    Article  MathSciNet  Google Scholar 

  32. Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems, vol. 6, pp. 3–10. Morgan-Kaufmann (1994)

    Google Scholar 

  33. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http://www.deeplearningbook.org

  34. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

  35. Zhou, L., Yan, Y., Qin, X., Yuan, C., Que, D., Wang, L.: Deep learning-based classification of massive electrocardiography data. In: 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 780–785 (2016)

    Google Scholar 

  36. Makhzani, A., Frey, B.J.: k-Sparse autoencoders. CoRR abs/1312.5663 (2013). http://arxiv.org/abs/1312.5663

  37. An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. In: Special Lecture on IE (2015)

    Google Scholar 

  38. Chazal, P., Heneghan, C., Sheridan, E., Reilly, R., Nolan, P., O’Malley, M.: Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans. Bio-med. Eng. 50, 686–96 (2003)

    Google Scholar 

  39. Sahakian, A.V., Petrutiu, S., Swiryn, S.: Abrupt changes in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans. EP Europace 9(7), 466–470 (2007)

    Google Scholar 

  40. Greenwald, S., Patil, R., Mark, R.: Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information (1990)

    Google Scholar 

  41. American Heart Association ECG Database. www.ecri.org/american-heart-association-ecg-database-usb/. Accessed 15 January 2019

  42. Jeni, L., Cohn, J., De la Torre, F.: Facing imbalanced data - recommendations for the use of performance metrics. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, vol. 2013, September 2013

    Google Scholar 

Download references

Acknowledgements

The research presented in this paper was supported by the funds assigned to AGH University of Science and Technology by the Polish Ministry of Science and Higher Education. It has been carried out as part of the Industrial Doctorate Programme, in cooperation between Comarch and AGH University of Science and Technology. We would like to thank the management of Comarch e-Healthcare Department for a permission to publish this study.

Author information

Authors and Affiliations

  1. Healthcare Department, Comarch SA, al. Jana Pawła II 39a, 31-864, Krakow, Poland

    Jan Garus, Mateusz Pabian & Marcin Wisniewski

  2. AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Krakow, Poland

    Jan Garus, Mateusz Pabian & Bartlomiej Sniezynski

Authors
  1. Jan Garus
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mateusz Pabian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcin Wisniewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bartlomiej Sniezynski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jan Garus .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Garus, J., Pabian, M., Wisniewski, M., Sniezynski, B. (2021). Electrocardiogram Quality Assessment with Autoencoder. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77967-2_58

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77966-5

  • Online ISBN: 978-3-030-77967-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation