Home Mathematics Personalization of parameters of electro-physiological model of the human heart
Article
Licensed
Unlicensed Requires Authentication

Personalization of parameters of electro-physiological model of the human heart

  • Vasilii Yu. Kabak EMAIL logo , Roman O. Rokeakh , Tatyana M. Nesterova , Maksim A. Dzhigil , Tatyana V. Chumarnaya , Arsenii D. Dokuchaev and Olga E. Solovyova
Published/Copyright: November 6, 2025

Abstract

Mathematical modelling in personalized medicine requires the identification of model parameters characterizing the clinical picture. Simplification and acceleration of this process will make it possible to implement modelling in routine clinical practice with greater efficiency. We have conducted a study of the dependencies between identified parameter (myocardial conductivity) and the activation time of 95% of the myocardium or the width of the QRS complex within a collection of personalized models of ventricles of patients with chronic heart failure and left bundle branch block, as well as varying degrees of structural and functional damage to the myocardium. A simplified algorithm for determining individual myocardial conductivity based on clinical features of a patient’s electrocardiogram using regression models has been proposed. The results show high accuracy in predicting QRSd values using the proposed algorithm, which demonstrates the possibility to facilitate significantly the personalization of model parameters for their wide application.

MSC 2010: 92C50; 65K10; 62J05; 62P10

Funding statement: The work was supported by the Russian Science Foundation, project No. 24–15–00335.

References

[1] J. D. Bayer, R. C. Blake, G. Plank, and N. A. Trayanova, Optimizing cardiac resynchronization therapy: an update on new insights and advancements. Curr. Heart Fail. Rep. 15 (2018), 2243–2254.Search in Google Scholar

[2] J. D. Bayer, A. J. Prassl, A. Pashaei, J. F. Gomez, A. Frontera, A. Neic, G. Plank, and E. J. Vigmond, Universal ventricular coordinates: a generic framework for describing position within the heart and transferring data. Medical Image Analysis 45 (2018), 83–93.10.1016/j.media.2018.01.005Search in Google Scholar PubMed

[3] R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comp. 16(5) (1995), 1190–1208.10.1137/0916069Search in Google Scholar

[4] E. F. Carpio, J. F. Gomez, R. Sebastian, A. Lopez-Perez, E. Castellanos, J. Almendral, J, M. Ferrero, and B. Trenor, Optimization of lead placement in the right ventricle during cardiac resynchronization therapy. A simulation study. Frontiers in Physiology 10 (2019).10.3389/fphys.2019.00074Search in Google Scholar PubMed PubMed Central

[5] M. D. Cerqueira, N. J. Weissman, D. Vasken, A. K. Jacobs, S. Kaul, W. K. Laskey, D. J. Pennell, J. A. Rumberger, T. Ryan, and M. S. Verani, Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 105 (2002), No. 4, 539–542.10.1161/hc0402.102975Search in Google Scholar PubMed

[6] C. M. Costa, P. Gemmell, M. K. Elliott, J. Whitaker, F. O. Campos, M. Strocchi, A. Neic, K. Gillette, E. Vigmond, G. Plank, R. Razavi, M. O’Neill, C. A. Rinaldi, and M. J. Bishop, Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction. Computers in Biology and Medicine 141 (2022), 105061.10.1016/j.compbiomed.2021.105061Search in Google Scholar PubMed PubMed Central

[7] F. S. Costabal, D. E. Hurtado and E. Kuhl, Generating Purkinje networks in the human heart. J. Biomech. 49 (2016), No. 12, 2455–2465.10.1016/j.jbiomech.2015.12.025Search in Google Scholar PubMed PubMed Central

[8] A. Dokuchaev, T. Chumarnaya, A. Bazhutina, S. Khamzin, V. Lebedeva, T. Lyubimtseva, S. Zubarev, D. Lebedev, and O. Solovyova, Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy. Frontiers in Physiology 14 (2023), 1162520.10.3389/fphys.2023.1162520Search in Google Scholar PubMed PubMed Central

[9] M. Glikson, J. C. Nielsen, M. B. Kronborg, Y. Michowitz, A. Auricchio, I. M. Barbash, J. A. Barrabés, G. Boriani, F. Braunschweig, M. Brignole, H. Burri, A. J. S. Coats, J. C. Deharo, V. Delgado, G. P. Diller, C. W. Israel, A. Keren, R. E. Knops, D. Kotecha, C. Leclercq, B. Merkely, C. Starck, I. Thylén, J. M. Tolosana, ESC Scientific Document Group, 2021 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy: Developed by the Task Force on cardiac pacing and cardiac resynchronization therapy of the European Society of Cardiology (ESC) With the special contribution of the European Heart Rhythm Association (EHRA). European Heart Journal 42 (2021), No. 35, 3427–3520.10.1016/j.rec.2022.04.004Search in Google Scholar PubMed

[10] A. Grimaldi, E. Z. Gorodeski, and J. Rickard, Optimizing cardiac resynchronization therapy: an update on new insights and advancements. Curr. Heart Fail. Rep. 15 (2018), 156–160.10.1007/s11897-018-0391-ySearch in Google Scholar PubMed

[11] S. Khamzin, A. Dokuchaev, A. Bazhutina, T. Chumarnaya, S. Zubarev, T. Lyubimtseva, V. Lebedeva, D. Lebedev, V. Gurev, and O. Solovyova, Machine learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data. Frontiers in Physiology 12 (2021), 753282.10.3389/fphys.2021.753282Search in Google Scholar PubMed PubMed Central

[12] J. Melgaard, P. M. van Dam, A. Sommer, P. Fruelund, J. C. Nielsen, S. Riahi, and C. Graff, Non-invasive estimation of QLV from the standard 12-lead ECG in patients with left bundle branch block. Frontiers in Physiology 13 (2022).10.3389/fphys.2022.939240Search in Google Scholar PubMed PubMed Central

[13] A. Mincholé, E. Zacur, R. Ariga, V. Grau, and B. Rodriguez, MRI-based computational torso/biventricular multiscale models to investigate the impact of anatomical variability on the ECG QRS complex. Frontiers in Physiology 10 (2019), 1103.10.3389/fphys.2019.01103Search in Google Scholar PubMed PubMed Central

[14] S. Pezzuto, P. Kal’avský, M. Potse, F. W. Prinzen, A. Auricchio, and R. Krause, Evaluation of a rapid anisotropic model for ECG simulation. Frontiers in Physiology 8 (2017), 265.10.3389/fphys.2017.00265Search in Google Scholar PubMed PubMed Central

[15] F. Plesinger, A. M. W. van Stipdonk, R. Smisek, J. Halamek, P. Jurak, A. H. Maass, M. Meine, K. Vernooy, and F. W. Prinzen, Fully automated QRS area measurement for predicting response to cardiac resynchronization therapy. J. Electrocardiology 63 (2020), 159–163.10.1016/j.jelectrocard.2019.07.003Search in Google Scholar PubMed

[16] M. Strocchi, K. Gillette, A. Neic, M. K. Elliott, N. Wijesuriya, V. Mehta, E. J. Vigmond, G. Plank, C. A. Rinaldi, and S. A. Niederer, Comparison between conduction system pacing and cardiac resynchronization therapy in right bundle branch block patients. Frontiers in Physiology 13 (2022), 1011566.10.3389/fphys.2022.1011566Search in Google Scholar PubMed PubMed Central

[17] N. Y. Tan, C. M. Witt, J. K. Oh, and Y. M. Cha, Left bundle branch block. Circulation: Arrhythmia and Electrophysiology 13 (2020), No. 4, e008239.10.1161/CIRCEP.119.008239Search in Google Scholar PubMed

[18] K. H. W. J. T. Tusscher and A. V. Panfilov, Alternans and spiral breakup in a human ventricular tissue model. Amer. J. Physiology-Heart and Circulatory Physiology 291 (2006), 1088–1100.10.1152/ajpheart.00109.2006Search in Google Scholar PubMed

Received: 2025-03-01
Revised: 2025-04-10
Accepted: 2025-08-26
Published Online: 2025-11-06
Published in Print: 2025-11-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 15.1.2026 from https://www.degruyterbrill.com/document/doi/10.1515/rnam-2025-0026/html?lang=en
Scroll to top button