Mathematical models of electrocardiosignals for the task of their simulation, taking into account various types of artifacts

##plugins.themes.bootstrap3.article.main##

Andriy Kondratiuk
Iaroslav Lytvynenko

Анотація

This article presents the developed new stochastic mathematical models of the electrocardiosignal, which allow to take into account the randomness and cyclicity of its physical nature and the possibility of taking into account various types of artifacts, which may be due to various reasons (zero drift of the isopotential line, poor contact of the electrodes with the patient's body surface, etc.). The artifacts that allow to take into account mathematical models primarily include those that manifest themselves in a change in the shape of the electrocardiosignal, that is, atypical changes in morphology not associated with signs of pathologies or signs of a normal signal. These atypical changes are taken into account by mathematical models both on cycle segments and on individual segments-zones of the electrocardiosignal. In addition, the developed mathematical models allow to take into account changes in the rhythm of such a cyclic signal by taking it into account in the rhythm function, and therefore, in addition to artifacts that manifest themselves in the morphology of the signal, mathematical models allow to lay down possible artifacts associated with the rhythm during modeling.

##plugins.themes.bootstrap3.article.details##

Розділ

Articles

Посилання

1. Moody G.B., Mark R.G. The impact of the MIT-BIH arrhythmia database // IEEE Engineering in Medicine and Biology Magazine. 2001. Vol. 20, No. 3. P. 45-50.

2. Simulation of cyclic signals (Generalized approach). Lupenko, S., Lytvynenko, I., Hotovych, V. CEUR Workshop Proceedings, 2021, 3038, pp. 86–92

3. Yang C, Cao Y, Li P, Yang Y, Xiang M. Computational Modeling of Cardiac Electrophysiology with Human Realistic Heart-Torso Model. Bioengineering (Basel). 2025 Apr 6;12(4):392. doi: 10.3390/bioengineering12040392. PMID: 40281752; PMCID: PMC12025261.

4. Evolution of mathematical models of cardiomyocyte electrophysiology Bogdan Amuzescu, Razvan Airini, Florin Bogdan Epureanu, Stefan A. Mann, Thomas Knott, Beatrice Mihaela Radu. Mathematical Biosciences. Volume 334, April 2021, 108567. https://doi.org/10.1016/j.mbs.2021.108567

5. Clifford G.D., Azuaje F., McSharry P. Advanced Methods and Tools for ECG Data Analysis. Boston : Artech House, 2006. 408 p.

6. Adib E., Afghah F., Prevost J.J. Synthetic ECG signal generation using generative neural networks // PLoS ONE. 2025. - Vol. 20, No. 3. - Article e0271270. https://doi.org/10.1371/journal.pone.0271270.

7. Bachi L., et al. ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions // IEEE Transactions on Biomedical Engineering. 2023. - Vol. 70, No. 12. P. 3449-3460. doi:10.1109/TBME.2023.3288701.

8. Golany T., Radinsky K. PGANs: Personalized generative adversarial networks for ECG synthesis // Machine Learning for Healthcare Conference. 2019. P. 1-16.

9. Zhu F., Ye M., Fu Y. Electrocardiogram generation with a bidirectional LSTM-conditional GAN // Scientific Reports. 2019. - Vol. 9. Article 6734.

10. Pöhl P., Schlegel V., Li H., Bharath A. Generating Realistic Multi-Beat ECG Signals. 2025. doi:10.48550/arXiv.2505.18189.

11. McSharry P.E., Clifford G.D., Tarassenko L., Smith L. A dynamical model for generating synthetic electrocardiogram signals // IEEE Transactions on Biomedical Engineering. 2003. - Vol. 50, No. 3. P. 289–294.

12. Goodfellow I., Pouget-Abadie J., Mirza M. Generative adversarial networks // Advances in Neural Information Processing Systems. 2014. P. 2672-2680.

13. Delaney A.M., Brophy E., Ward T. Synthetic ECG generation using GANs // IEEE Engineering in Medicine and Biology Society Conference. 2019. P. 3893-3896.

14. Luo R., Zhao Y., Zhang Y. Synthetic ECG signal generation based on Wasserstein GAN // Applied Sciences. 2023. - Vol. 13.

15. Lopez-Alcaraz J., Strodthoff N. Diffusion-based conditional ECG generation with structured state space models // Computers in Biology and Medicine. - 2023. - Vol. 163.

16. Li X., Xu S., Habib F., Aminnejad N., Gupta A., Huang H. CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals. 2025. doi:10.48550/arXiv.2502.17536.

17. Lytvynenko I., Horkunenko A., Kuchvara O., Palaniza Y. Methods of processing cyclic signals in automated cardiodiagnostic complexes . Proceedings of the 1st International Workshop on Information-Communication Technologies & Embedded Systems, (ICT&ES-2019), Mykolaiv, November 13-14, 2019, Ukraine, 2019. P.116-127.

18. I.V. Lytvynenko. The method of segmentation of stochastic cyclic signals for the problems of their processing and modeling/ I.V. Lytvynenko / Journal of Hydrocarbon Power Engineering, Oil and Gas Measurement and Testing. 2017, Vol. 4, No. 2, pp. 93-103.

Статті цього автора (цих авторів), які найбільше читають