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

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.Suche 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.2018.01.005Suche 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), 1190–1208.10.1137/0916069Suche 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.2019.00074Suche 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/hc0402.102975Suche 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.2021.105061Suche 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.2015.12.025Suche 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.2023.1162520Suche 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.2022.04.004Suche 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-ySuche 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.2021.753282Suche 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.2022.939240Suche 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.2019.01103Suche 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.2017.00265Suche 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.2019.07.003Suche 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.2022.1011566Suche 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.119.008239Suche 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.00109.2006Suche in Google Scholar PubMed