Skip to main content

Synthesis of Standard 12-Lead ECG from Single-Lead ECG Using Shifted Diffusion Models

  • Conference paper
  • First Online:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14949))

  • 646 Accesses

Abstract

As the primary tool for monitoring cardiac health, a standard 12-lead ECG device is specialized medical equipment that is challenging to integrate into daily life. Meanwhile, existing portable ECG monitoring devices can only capture single-lead ECG, which is insufficient for health diagnosis. To address this issue, we propose a novel shifted diffusion model algorithm that utilizes a single-lead ECG to generate a standard 12-lead ECG. Our algorithm uses the detected single-lead ECG as the condition and employs the diffusion model to synthesize corresponding other 11-lead ECG. The extra shift is utilized in the forward process so that the model can learn better. Our approach has been tested on three datasets, yielding promising results.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://tianchi.aliyun.com/competition/entrance/231754/information?lang=en-us.

References

  1. Gaziano, T., Reddy, K.S., Paccaud, F, et al.: Cardiovascular disease. Disease Control Priorities in Developing Countries, 2nd edn. (2006)

    Google Scholar 

  2. Dahlöf, B.: Cardiovascular disease risk factors: epidemiology and risk assessment. Am. J. Cardiol. 105(1), 3A-9A (2010)

    Article  Google Scholar 

  3. De Bacquer, D., De Backer, G., Kornitzer, M., et al.: Prognostic value of ECG findings for total, cardiovascular disease, and coronary heart disease death in men and women[J]. Heart 80(6), 570–577 (1998)

    Article  Google Scholar 

  4. Luz, E.J.S., Schwartz, W.R., Cámara-Chávez, G., et al.: ECG-based heartbeat classification for arrhythmia detection: a survey. Comput. Methods Programs Biomed. 127, 144–164 (2016)

    Article  Google Scholar 

  5. Berkaya, S.K., Uysal, A.K., Gunal, E.S., et al.: A survey on ECG analysis. Biomed. Signal Process. Control 43, 216–235 (2018)

    Article  Google Scholar 

  6. Denes, P.: The importance of derived 12-lead electrocardiography in the interpretation of arrhythmias detected by Holter recording[J]. Am. Heart J. 124(4), 905–911 (1992)

    Article  Google Scholar 

  7. Vogiatzis, I., Koulouris, E., Ioannidis, A., et al.: The importance of the 15-lead versus 12-lead ECG recordings in the diagnosis and treatment of right ventricle and left ventricle posterior and lateral wall acute myocardial infarctions[J]. Acta Informatica Medica 27(1), 35 (2019)

    Article  Google Scholar 

  8. Mortara, J.L.: ECG acquisition and signal processing: 12-lead ECG acquisition. Cardiac Safety of Noncardiac Drugs: Practical Guidelines for Clinical Research and Drug Development, pp. 131–145. Humana Press, Totowa, NJ (2005)

    Google Scholar 

  9. Maheshwari, S., Acharyya, A., Rajalakshmi, P., et al.: Accurate and reliable 3-lead to 12-lead ECG reconstruction methodology for remote health monitoring applications. IRBM 35(6), 341–350 (2014)

    Article  Google Scholar 

  10. Mehta, D.D., Nazir, N.T., Trohman, R.G., et al.: Single-lead portable ECG devices: perceptions and clinical accuracy compared to conventional cardiac monitoring. J. Electrocardiol. 48(4), 710–716 (2015)

    Article  Google Scholar 

  11. Boriani, G., Palmisano, P., Malavasi, V.L., et al.: Clinical factors associated with atrial fibrillation detection on single-time point screening using a hand-held single-lead ECG device. J. Clin. Med. 10(4), 729 (2021)

    Article  Google Scholar 

  12. Gifari, M.W., Zakaria, H., Mengko, R.: Design of ECG Homecare: 12-lead ECG acquisition using single channel ECG device developed on AD8232 analog front end. In: 2015 International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, pp. 371–376 (2015)

    Google Scholar 

  13. Lee, H.J., Lee, D.S., Kwon, H.B., et al.: Reconstruction of 12-lead ECG using a single-patch device. Methods Inf. Med. 56(04), 319–327 (2017)

    Article  Google Scholar 

  14. Khunti, K.: Accurate interpretation of the 12-lead ECG electrode placement: a systematic review. Health Educ. J. 73(5), 610–623 (2014)

    Article  Google Scholar 

  15. Atoui, H., Fayn, J., Rubel, P.: A neural network approach for patient-specific 12-lead ECG synthesis in patient monitoring environments. IEEE Comput. Cardiology 2004, 161–164 (2004)

    Google Scholar 

  16. Kachenoura, A., Porée, F., Carrault, G., et al.: Non-linear 12-lead ECG synthesis from two intracardiac recordings. In: 2009 36th Annual Computers in Cardiology Conference (CinC), pp. 577–580. IEEE (2009)

    Google Scholar 

  17. Gundlapalle, V., Acharyya, A.: A Novel Single Lead to 12-Lead ECG reconstruction methodology using convolutional neural networks and LSTM. In: 2022 IEEE 13th Latin America Symposium on Circuits and System (LASCAS), pp. 01–04. IEEE (2022)

    Google Scholar 

  18. Seo, H.C., Yoon, G.W., Joo, S., et al.: Multiple electrocardiogram generator with single-lead electrocardiogram. Comput. Methods Programs Biomed. 221, 106858 (2022)

    Article  Google Scholar 

  19. Chen, J., Zheng, X., Yu, H., et al.: Electrocardio panorama: synthesizing new ECG views with self-supervision. arXiv preprint arXiv:2105.06293, 2021

  20. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)

    Google Scholar 

  21. Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)

  22. Tashiro, Y., Song, J., Song, Y., et al.: Csdi: conditional score-based diffusion models for probabilistic time series imputation. Adv. Neural. Inf. Process. Syst. 34, 24804–24816 (2021)

    Google Scholar 

  23. Alcaraz, J.M.L., Strodthoff, N.: Diffusion-based time series imputation and forecasting with structured state space models. arXiv preprint arXiv:2208.09399 (2022)

  24. Shen, L., Kwok, J.: Non-autoregressive conditional diffusion models for time series prediction. In: International Conference on Machine Learning. PMLR, pp. 31016–31029 (2023)

    Google Scholar 

  25. Adib, E., Fernandez, A.S., Afghah, F., et al.: Synthetic ecg signal generation using probabilistic diffusion models. IEEE Access (2023)

    Google Scholar 

  26. Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. PMLR, pp. 8162–8171 (2021)

    Google Scholar 

  27. Alcaraz, J.M.L., Strodthoff, N.: Diffusion-based conditional ECG generation with structured state space models. Comput. Biol. Med. 163, 107115 (2023)

    Article  Google Scholar 

  28. Gu, A., Goel, K., Ré, C.: Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396 (2021)

  29. Zama, M.H., Schwenker, F.: ECG synthesis via diffusion-based state space augmented transformer. Sensors 23(19), 8328 (2023)

    Article  Google Scholar 

  30. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., et al.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning. PMLR, pp. 2256–2265 (2015)

    Google Scholar 

  31. Song, Y., Sohl-Dickstein, J., Kingma, D.P., et al.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

  32. Kong, Z., Ping, W., Huang, J., et al.: Diffwave: a versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020)

  33. Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. Advances in neural information processing systems, 30 (2017)

    Google Scholar 

  34. Wagner, P., Strodthoff, N., Bousseljot, R.D., et al.: PTB-XL, a large publicly available electrocardiography dataset. Sci. Data 7(1), 1–15 (2020)

    Article  Google Scholar 

  35. Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)

    Article  Google Scholar 

  36. Matyschik, M., Mauranen, H., Bonizzi, P., et al.: Feasibility of ECG reconstruction from minimal lead sets using convolutional neural networks. In: 2020 Computing in Cardiology. IEEE, pp. 1–4 (2020)

    Google Scholar 

  37. Sohn, J., Yang, S., Lee, J., et al.: Reconstruction of 12-lead electrocardiogram from a three-lead patch-type device using a LSTM network. Sensors 20(11), 3278 (2020)

    Article  Google Scholar 

  38. Driemel, A., Krivošija, A., Sohler, C.: Clustering time series under the Fréchet distance. In: Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, pp. 766–785 (2016)

    Google Scholar 

  39. Kachuee, M., Fazeli, S., Sarrafzadeh, M.: Ecg heartbeat classification: a deep transferable representation. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 443–444. IEEE (2018)

    Google Scholar 

  40. SimGANs: Simulator-based generative adversarial networks for ECG synthesis to improve deep ECG classification

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 62172018, No. 62102008) and Wuhan East Lake High-Tech Development Zone National Comprehensive Experimental Base for Governance of Intelligent Society.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shenda Hong .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Liu, J., Li, H., Hong, S. (2024). Synthesis of Standard 12-Lead ECG from Single-Lead ECG Using Shifted Diffusion Models. In: Bifet, A., Krilavičius, T., Miliou, I., Nowaczyk, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14949. Springer, Cham. https://doi.org/10.1007/978-3-031-70378-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-70378-2_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-70377-5

  • Online ISBN: 978-3-031-70378-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics