Estimation of high-density activation maps during atrial fibrillation

The study of activation maps using multi-electrode arrays (MEA) can help to understand atrial fibrillation (AF) mechanisms. Activation mapping based on recorded unipolar electrograms (u-EGM) rely on the local activation time (LAT) detector, which has a limited robustness, accuracy, and generally requires manual post-edition. In general, LAT detection ignores spatiotemporal information about activation and conduction conveyed by the relation between signals of the MEA sensor. This work proposes an approach to construct activation maps by simultaneous analysis of u-EGMs from small clusters of MEA electrodes. The algorithm iteratively fits an activation pattern model to the acquired data. Accuracy was evaluated by comparing with audited maps created by expert electrophysiologists from a patient undergoing open-chest surgery during AF. The estimation error was -0.29 ± 6.01 ms (236 maps, 28369 LATs) with high correlation (¿ = 0.93). Therefore, activation maps can be decomposed into local activation patterns derived from fitting an activation model, resulting in smooth and comprehensive high-density activation maps

Separating atrial near fields and atrial far fields in simulated intra-Atrial electrograms

The detailed characterization of complex forms of atrial flutter relies on the correct interpretation of intra-atrial electrograms. For this, the near fieldcomponents, which represent the local electrical activity, are decisive. However, far field components arising from distant electrical sources in the atria can obscure the diagnosis.We developed a method to separate and characterize atrial near field and atrial far field components from bipolar intra-atrial electrograms. First, a set of bipolar electrograms was created by simulating different propagation scenarios representing common clinical depolarizationpatterns. Second, near and far fields were detected as active segments usinga non-linear energy operator-based approach. Third, the maximum slope and the spectralpower were extracted as features for all active segments. Active segments were grouped accounting for both the timing and the location of their occurrence. In a last step, the active segments were classified in near and far fields by comparing their feature values to a threshold.All active segments were detected correctly. On average, near fields showed 15.1x larger maximum slopes and 40.4x larger spectral powers above 100 Hz than far fields. For 135 active segments detected in 72 bipolar electrograms, 5.2% and 6.7% were misclassified using the maximum slope and the spectral power, respectively. All active segments were classified correctly if only one near field segment was assumed to occur per electrogram.The separation of atrial near andatrial far fields was successfully developed and applied to in silico electrograms.Theseinvestigations providea promising basis fora future clinical study to ultimatelyfacilitatethe precise clinical diagnosis of atrial flutter

Solving Inaccuracies in Anatomical Models for Electrocardiographic Inverse Problem Resolution by Maximizing Reconstruction Quality

[EN] Electrocardiographic Imaging has become an increasingly used technique for non-invasive diagnosis of cardiac arrhythmias, although the need for medical imaging technology to determine the anatomy hinders its introduction in the clinical practice. This paper explores the ability of a new metric based on the inverse reconstruction quality for the location and orientation of the atrial surface inside the torso. Body surface electrical signals from 31 realistic mathematical models and four AF patients were used to estimate the optimal position of the atria inside the torso. The curvature of the L-curve from the Tikhonov method, which was found to be related to the inverse reconstruction quality, was measured after application of deviations in atrial position and orientation. Independent deviations in the atrial position were solved by finding the maximal L-curve curvature with an error of 1.7 +/- 2.4 mm in mathematical models and 9.1 +/- 11.5 mm in patients. For the case of independent angular deviations, the error in location by using the L-curve was 5.8 +/- 7.1 degrees in mathematical models and 12.4 degrees +/- 13.2 degrees in patients. The ability of the L-curve curvature was tested also under superimposed uncertainties in the three axis of translation and in the three axis of rotation, and the error in location was of 2.3 +/- 3.2 mm and 6.4 degrees +/- 7.1 degrees in mathematical models, and 7.9 +/- 10.7 mm and 12.1 degrees +/- 15.5 degrees in patients. The curvature of L-curve is a useful marker for the atrial position and would allow emending the inaccuracies in its location.This work was supported in part by Generalitat Valenciana under Grant ACIF/2013/021, in part by the Instituto de Salud Carlos III, Ministry of Economy and Competitiveness, Spain, under Grant PI13-01882, Grant PI13-00903, Grant PI14/00857, Grant TEC2013-46067-R, and Grant DTS16/00160, in part by the Spanish Society of Cardiology (Grant for Clinical Research in Cardiology 2015), and in part by the Spanish Ministry of Science and Innovation (Red RIC) under Grant PLE2009-0152.Rodrigo Bort, M.; Climent, AM.; Liberos Mascarell, A.; Hernández-Romero, I.; Arenal, A.; Bermejo, J.; Fernández-Avilés, F.... (2018). Solving Inaccuracies in Anatomical Models for Electrocardiographic Inverse Problem Resolution by Maximizing Reconstruction Quality. IEEE Transactions on Medical Imaging. 37(3):733-740. https://doi.org/10.1109/TMI.2017.2707413S73374037

Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms

[EN] [Background] Phase mapping has become a broadly used technique to identify atrial reentrant circuits for ablative therapy guidance. This work studies the phase mapping process and how the signal nature and its filtering affect the reentrant pattern characterization in electrogram (EGM), body surface potential mapping, and electrocardiographic imaging signals. [Methods and Results] EGM, body surface potential mapping, and electrocardiographic imaging phase maps were obtained from 17 simulations of atrial fibrillation, atrial flutter, and focal atrial tachycardia. Reentrant activity was identified by singularity point recognition in raw signals and in signals after narrow band-pass filtering at the highest dominant frequency (HDF). Reentrant activity was dominantly present in the EGM recordings only for atrial fibrillation and some atrial flutter propagations patterns, and HDF filtering allowed increasing the reentrant activity detection from 60% to 70% of time in atrial fibrillation in unipolar recordings and from 0% to 62% in bipolar. In body surface potential mapping maps, HDF filtering increased from 10% to 90% the sensitivity, although provoked a residual false reentrant activity ¿30% of time. In electrocardiographic imaging, HDF filtering allowed to increase ¿100% the time with detected rotors, although provoked the apparition of false rotors during 100% of time. Nevertheless, raw electrocardiographic imaging phase maps presented reentrant activity just in atrial fibrillation recordings accounting for ¿80% of time. [Conclusions] Rotor identification is accurate and sensitive and does not require additional signal processing in measured or noninvasively computed unipolar EGMs. Bipolar EGMs and body surface potential mapping do require HDF filtering to detect rotors at the expense of a decreased specificity.This study was supported, in part, by Universitat Politecnica de Valencia through its research initiative program; Generalitat Valenciana Grants (ACIF/2013/021); the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-01882, PI13-00903, PI14/00857, PI16/01123, TEC2013-46067-R, DTS16/0160, and IJCI-2014-22178); Spanish Society of Cardiology (Grant for Clinical Research in Cardiology 2015); Spanish Ministry of Science and Innovation (Red RIC RD12.0042.0001); and the National Heart, Lung, and Blood Institute (P01-HL039707, P01-HL087226, and Q1 R01-HL118304) and cofounded by FEDER.Rodrigo Bort, M.; Martínez Climent, A.; Liberos Mascarell, A.; Fernández-Avilés, F.; Berenfeld, O.; Atienza, F.; Guillem Sánchez, MS. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation Arrhythmia and Electrophysiology. 10(9):1-13. https://doi.org/10.1161/CIRCEP.117.005008S113109Allessie, M., & de Groot, N. (2014). CrossTalk opposing view: Rotors have not been demonstrated to be the drivers of atrial fibrillation. The Journal of Physiology, 592(15), 3167-3170. doi:10.1113/jphysiol.2014.271809Narayan, S. M., & Zaman, J. A. B. (2016). Mechanistically based mapping of human cardiac fibrillation. The Journal of Physiology, 594(9), 2399-2415. doi:10.1113/jp270513Guillem, M. S., Climent, A. M., Rodrigo, M., Fernández-Avilés, F., Atienza, F., & Berenfeld, O. (2016). Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications. Cardiovascular Research, 109(4), 480-492. doi:10.1093/cvr/cvw011Narayan, S. M., Krummen, D. E., Clopton, P., Shivkumar, K., & Miller, J. M. (2013). Direct or Coincidental Elimination of Stable Rotors or Focal Sources May Explain Successful Atrial Fibrillation Ablation. Journal of the American College of Cardiology, 62(2), 138-147. doi:10.1016/j.jacc.2013.03.021Berenfeld, O., Ennis, S., Hwang, E., Hooven, B., Grzeda, K., Mironov, S., … Jalife, J. (2011). Time- and frequency-domain analyses of atrial fibrillation activation rate: The optical mapping reference. Heart Rhythm, 8(11), 1758-1765. doi:10.1016/j.hrthm.2011.05.007Gray, R. A., Pertsov, A. M., & Jalife, J. (1998). Spatial and temporal organization during cardiac fibrillation. Nature, 392(6671), 75-78. doi:10.1038/32164Rodrigo, M., Guillem, M. S., Climent, A. M., Pedrón-Torrecilla, J., Liberos, A., Millet, J., … Berenfeld, O. (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm, 11(9), 1584-1591. doi:10.1016/j.hrthm.2014.05.013Vijayakumar, R., Vasireddi, S. K., Cuculich, P. S., Faddis, M. N., & Rudy, Y. (2016). Methodology Considerations in Phase Mapping of Human Cardiac Arrhythmias. Circulation: Arrhythmia and Electrophysiology, 9(11). doi:10.1161/circep.116.004409Guillem, M. S., Climent, A. M., Millet, J., Arenal, Á., Fernández-Avilés, F., Jalife, J., … Berenfeld, O. (2013). Noninvasive Localization of Maximal Frequency Sites of Atrial Fibrillation by Body Surface Potential Mapping. Circulation: Arrhythmia and Electrophysiology, 6(2), 294-301. doi:10.1161/circep.112.000167Haissaguerre, M., Hocini, M., Denis, A., Shah, A. J., Komatsu, Y., Yamashita, S., … Dubois, R. (2014). Driver Domains in Persistent Atrial Fibrillation. Circulation, 130(7), 530-538. doi:10.1161/circulationaha.113.005421Dössel, O., Krueger, M. W., Weber, F. M., Wilhelms, M., & Seemann, G. (2012). Computational modeling of the human atrial anatomy and electrophysiology. Medical & Biological Engineering & Computing, 50(8), 773-799. doi:10.1007/s11517-012-0924-6Koivumäki, J. T., Seemann, G., Maleckar, M. M., & Tavi, P. (2014). In Silico Screening of the Key Cellular Remodeling Targets in Chronic Atrial Fibrillation. PLoS Computational Biology, 10(5), e1003620. doi:10.1371/journal.pcbi.1003620Garcia-Molla, V. M., Liberos, A., Vidal, A., Guillem, M. S., Millet, J., Gonzalez, A., … Climent, A. M. (2014). Adaptive step ODE algorithms for the 3D simulation of electric heart activity with graphics processing units. Computers in Biology and Medicine, 44, 15-26. doi:10.1016/j.compbiomed.2013.10.023Rodrigo, M., Climent, A. M., Liberos, A., Calvo, D., Fernández-Avilés, F., Berenfeld, O., … Guillem, M. S. (2016). Identification of Dominant Excitation Patterns and Sources of Atrial Fibrillation by Causality Analysis. Annals of Biomedical Engineering, 44(8), 2364-2376. doi:10.1007/s10439-015-1534-xPEDRÓN-TORRECILLA, J., RODRIGO, M., CLIMENT, A. M., LIBEROS, A., PÉREZ-DAVID, E., BERMEJO, J., … GUILLEM, M. S. (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 27(4), 435-442. doi:10.1111/jce.12931Zlochiver, S., Yamazaki, M., Kalifa, J., & Berenfeld, O. (2008). Rotor meandering contributes to irregularity in electrograms during atrial fibrillation. Heart Rhythm, 5(6), 846-854. doi:10.1016/j.hrthm.2008.03.010ALHUSSEINI, M., VIDMAR, D., MECKLER, G. L., KOWALEWSKI, C. A., SHENASA, F., WANG, P. J., … RAPPEL, W.-J. (2017). Two Independent Mapping Techniques Identify Rotational Activity Patterns at Sites of Local Termination During Persistent Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 28(6), 615-622. doi:10.1111/jce.1317

Atrial location optimization by electrical measures for Electrocardiographic Imaging

[EN] Background: The Electrocardiographic Imaging (ECGI) technique, used to non-invasively reconstruct the epicardial electrical activity, requires an accurate model of the atria and torso anatomy. Here we evaluate a new automatic methodology able to locate the atrial anatomy within the torso based on an intrinsic electrical parameter of the ECGI solution. Methods: In 28 realistic simulations of the atrial electrical activity, we randomly displaced the atrial anatomy for +/- 2.5 cm and +/- 30 degrees on each axis. An automatic optimization method based on the L-curve curvature was used to estimate the original position using exclusively non-invasive data. Results: The automatic optimization algorithm located the atrial anatomy with a deviation of 0.5 +/- 0.5 cm in position and 16.0 +/- 10.7 degrees in orientation. With these approximate locations, the obtained electrophysiological maps reduced the average error in atrial rate measures from 1.1 +/- 1.1 Hz to 0.5 +/- 1.0 Hz and in the phase singularity position from 7.2 +/- 4.0 cm to 1.6 +/- 1.7 cm (p < 0.01). Conclusions: This proposed automatic optimization may help to solve spatial inaccuracies provoked by cardiac motion or respiration, as well as to use ECGI on torso and atrial anatomies from different medical image systems.This work was supported in part by: Generalitat Valenciana Grants [APOSTD/2017] and projects [GVA/2018/103]; Nvidia Corporation with GPU QUADRO P6000 donation.Gisbert Soler, V.; Jiménez-Serrano, S.; Roses-Albert, E.; Rodrigo Bort, M. (2020). Atrial location optimization by electrical measures for Electrocardiographic Imaging. Computers in Biology and Medicine. 127:1-8. https://doi.org/10.1016/j.compbiomed.2020.104031S18127Cuculich, P. S., Zhang, J., Wang, Y., Desouza, K. A., Vijayakumar, R., Woodard, P. K., & Rudy, Y. (2011). The Electrophysiological Cardiac Ventricular Substrate in Patients After Myocardial Infarction. Journal of the American College of Cardiology, 58(18), 1893-1902. doi:10.1016/j.jacc.2011.07.029Revishvili, A. S., Wissner, E., Lebedev, D. S., Lemes, C., Deiss, S., Metzner, A., … Kuck, K.-H. (2015). Validation of the mapping accuracy of a novel non-invasive epicardial and endocardial electrophysiology system. Europace, 17(8), 1282-1288. doi:10.1093/europace/euu339Haissaguerre, M., Hocini, M., Denis, A., Shah, A. J., Komatsu, Y., Yamashita, S., … Dubois, R. (2014). Driver Domains in Persistent Atrial Fibrillation. Circulation, 130(7), 530-538. doi:10.1161/circulationaha.113.005421PEDRÓN-TORRECILLA, J., RODRIGO, M., CLIMENT, A. M., LIBEROS, A., PÉREZ-DAVID, E., BERMEJO, J., … GUILLEM, M. S. (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 27(4), 435-442. doi:10.1111/jce.12931Cuculich, P. S., Wang, Y., Lindsay, B. D., Faddis, M. N., Schuessler, R. B., Damiano, R. J., … Rudy, Y. (2010). Noninvasive Characterization of Epicardial Activation in Humans With Diverse Atrial Fibrillation Patterns. Circulation, 122(14), 1364-1372. doi:10.1161/circulationaha.110.945709Wang, Y., Schuessler, R. B., Damiano, R. J., Woodard, P. K., & Rudy, Y. (2007). Noninvasive electrocardiographic imaging (ECGI) of scar-related atypical atrial flutter. Heart Rhythm, 4(12), 1565-1567. doi:10.1016/j.hrthm.2007.08.019Milan Horáček, B., & Clements, J. C. (1997). The inverse problem of electrocardiography: A solution in terms of single- and double-layer sources on the epicardial surface. Mathematical Biosciences, 144(2), 119-154. doi:10.1016/s0025-5564(97)00024-2Rodrigo, M., Climent, A. M., Liberos, A., Hernandez-Romero, I., Arenal, A., Bermejo, J., … Guillem, M. S. (2018). Solving Inaccuracies in Anatomical Models for Electrocardiographic Inverse Problem Resolution by Maximizing Reconstruction Quality. IEEE Transactions on Medical Imaging, 37(3), 733-740. doi:10.1109/tmi.2017.2707413Dössel, O., Krueger, M. W., Weber, F. M., Wilhelms, M., & Seemann, G. (2012). Computational modeling of the human atrial anatomy and electrophysiology. Medical & Biological Engineering & Computing, 50(8), 773-799. doi:10.1007/s11517-012-0924-6Koivumäki, J. T., Seemann, G., Maleckar, M. M., & Tavi, P. (2014). In Silico Screening of the Key Cellular Remodeling Targets in Chronic Atrial Fibrillation. PLoS Computational Biology, 10(5), e1003620. doi:10.1371/journal.pcbi.1003620Garcia-Molla, V. M., Liberos, A., Vidal, A., Guillem, M. S., Millet, J., Gonzalez, A., … Climent, A. M. (2014). Adaptive step ODE algorithms for the 3D simulation of electric heart activity with graphics processing units. Computers in Biology and Medicine, 44, 15-26. doi:10.1016/j.compbiomed.2013.10.023Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Highest dominant frequency and rotor positions are robust markers of driver location during noninvasive mapping of atrial fibrillation: A computational study. Heart Rhythm, 14(8), 1224-1233. doi:10.1016/j.hrthm.2017.04.017Dolan, E. D., Lewis, R. M., & Torczon, V. (2003). On the Local Convergence of Pattern Search. SIAM Journal on Optimization, 14(2), 567-583. doi:10.1137/s1052623400374495Rodrigo, M., Guillem, M. S., Climent, A. M., Pedrón-Torrecilla, J., Liberos, A., Millet, J., … Berenfeld, O. (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm, 11(9), 1584-1591. doi:10.1016/j.hrthm.2014.05.013Sanders, P., Berenfeld, O., Hocini, M., Jaïs, P., Vaidyanathan, R., Hsu, L.-F., … Haïssaguerre, M. (2005). Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans. Circulation, 112(6), 789-797. doi:10.1161/circulationaha.104.517011Atienza, F., Almendral, J., Ormaetxe, J. M., Moya, Á., Martínez-Alday, J. D., Hernández-Madrid, A., … Jalife, J. (2014). Comparison of Radiofrequency Catheter Ablation of Drivers and Circumferential Pulmonary Vein Isolation in Atrial Fibrillation. Journal of the American College of Cardiology, 64(23), 2455-2467. doi:10.1016/j.jacc.2014.09.053Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008Miller, J. M., Kalra, V., Das, M. K., Jain, R., Garlie, J. B., Brewster, J. A., & Dandamudi, G. (2017). Clinical Benefit of Ablating Localized Sources for Human Atrial Fibrillation. Journal of the American College of Cardiology, 69(10), 1247-1256. doi:10.1016/j.jacc.2016.11.079Perez-Alday, E. A., Thomas, J. A., Kabir, M., Sedaghat, G., Rogovoy, N., van Dam, E., … Tereshchenko, L. G. (2018). Torso geometry reconstruction and body surface electrode localization using three-dimensional photography. Journal of Electrocardiology, 51(1), 60-67. doi:10.1016/j.jelectrocard.2017.08.035Schulze, W. H. W., Mackens, P., Potyagaylo, D., Rhode, K., Tülümen, E., Schimpf, R., … Dössel, O. (2014). Automatic camera-based identification and 3-D reconstruction of electrode positions in electrocardiographic imaging. Biomedical Engineering / Biomedizinische Technik, 59(6). doi:10.1515/bmt-2014-0018Ghanem, R. N., Ramanathan, C., Ping Jia, & Rudy, Y. (2003). Heart-surface reconstruction and ecg electrodes localization using fluoroscopy, epipolar geometry and stereovision: application to noninvasive imaging of cardiac electrical activity. IEEE Transactions on Medical Imaging, 22(10), 1307-1318. doi:10.1109/tmi.2003.818263Lee, J., Thornhill, R. E., Nery, P., Robert deKemp, Peña, E., Birnie, D., … Ukwatta, E. (2019). Left atrial imaging and registration of fibrosis with conduction voltages using LGE-MRI and electroanatomical mapping. Computers in Biology and Medicine, 111, 103341. doi:10.1016/j.compbiomed.2019.103341Weiss, E., Wijesooriya, K., Dill, S. V., & Keall, P. J. (2007). Tumor and normal tissue motion in the thorax during respiration: Analysis of volumetric and positional variations using 4D CT. International Journal of Radiation Oncology*Biology*Physics, 67(1), 296-307. doi:10.1016/j.ijrobp.2006.09.009Wikström, K., Isacsson, U., Nilsson, K., & Ahnesjö, A. (2018). Reproducibility of heart and thoracic wall position in repeated deep inspiration breath holds for radiotherapy of left-sided breast cancer patients. Acta Oncologica, 57(10), 1318-1324. doi:10.1080/0284186x.2018.1490027Messinger-Rapport, B. J., & Rudy, Y. (1986). The Inverse Problem in Electrocardiography: A Model Study of the Effects of Geometry and Conductivity Parameters on the Reconstruction of Epicardial Potentials. IEEE Transactions on Biomedical Engineering, BME-33(7), 667-676. doi:10.1109/tbme.1986.325756Messinger-Rapport, B. J., & Rudy, Y. (1990). Noninvasive recovery of epicardial potentials in a realistic heart-torso geometry. Normal sinus rhythm. Circulation Research, 66(4), 1023-1039. doi:10.1161/01.res.66.4.1023Coll-Font, J., & Brooks, D. H. (2018). Tracking the Position of the Heart From Body Surface Potential Maps and Electrograms. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01727Van der Waal, J., Meijborg, V., Schuler, S., Coronel, R., & Oostendorp, T. (2020). In silico validation of electrocardiographic imaging to reconstruct the endocardial and epicardial repolarization pattern using the equivalent dipole layer source model. Medical & Biological Engineering & Computing, 58(8), 1739-1749. doi:10.1007/s11517-020-02203-yChamorro-Servent, J., Dubois, R., & Coudière, Y. (2019). Considering New Regularization Parameter-Choice Techniques for the Tikhonov Method to Improve the Accuracy of Electrocardiographic Imaging. Frontiers in Physiology, 10. doi:10.3389/fphys.2019.0027

Clinical Usefulness of Computational Modeling-Guided Persistent Atrial Fibrillation Ablation: Updated Outcome of Multicenter Randomized Study

Objective: Catheter ablation of persistent atrial fibrillation (AF) is still challenging, no optimal extra-pulmonary vein lesion set is known. We previously reported the clinical feasibility of computational modeling-guided AF catheter ablation. Methods: We randomly assigned 118 patients with persistent AF (77.8% men, age 60.8 ± 9.9 years) to the computational modeling-guided ablation group (53 patients) and the empirical ablation group (55 patients) based on the operators' experience. For virtual ablation, four virtual linear and one electrogram-guided lesion sets were tested on patient heart computed tomogram-based models, and the lesion set with the fastest termination time was reported to the operator in the modeling-guided ablation group. The primary outcome was freedom from atrial tachyarrhythmias lasting longer than 30 s after a single procedure. Results: During 31.5 ± 9.4 months, virtual ablation procedures were available in 95.2% of the patients (108/118). Clinical recurrence rate was significantly lower after a modeling-guided ablation than after an empirical ablation (20.8 vs. 40.0%, log-rank p = 0.042). Modeling-guided ablation was independently associated with a better long-term rhythm outcome of persistent AF ablation (HR = 0.29 [0.12-0.69], p = 0.005). The rhythm outcome of the modeling-guided ablation showed better trends in males, non-obese patients with a less remodeled atrium (left atrial dimension < 50 mm), ejection fraction ≥ 50%, and those without hypertension or diabetes (p < 0.01). There were no significant differences between the groups for the total procedure time (p = 0.403), ablation time (p = 0.510), and major complication rate (p = 0.900). Conclusion: Among patients with persistent AF, the computational modeling-guided ablation was superior to the empirical catheter ablation regarding the rhythm outcome. Clinical trial registration: This study was registered with the ClinicalTrials.gov, number NCT02171364.ope

A meshless fragile points method for rule-based definition of myocardial fiber orientation

Background and objective: Rule-based methods are commonly used to estimate the arrangement of myocardial fibers by solving the Laplace problem with appropriate Dirichlet boundary conditions. Existing algorithms are using the Finite Element Method (FEM) to solve the Laplace–Dirichlet problem. However, meshless methods are under development for cardiac electrophysiology simulation. The objective of this work is to propose a meshless rule based method for the determination of myocardial fiber arrangement without requiring a mesh discretization as it is required by FEM. Methods: The proposed method employs the Fragile Points Method (FPM) for the solution of the Laplace–Dirichlet problem. FPM uses simple discontinuous trial functions and single-point exact integration for linear trial functions that set it as a promising alternative to the Finite Element Method. We derive the FPM formulation of the Laplace–Dirichlet and we estimate ventricular and atrial fiber arrangements according to rules based on histology findings for four different geometries. The obtained fiber arrangements from FPM are compared with the ones obtained from FEM by calculating the angle between the fiber vector fields of the two methods for three different directions (i.e., longitudinal, sheet, transverse). Results:The fiber arrangements that were generated with FPM were in close agreement with the generated arrangements from FEM for all three directions. The mean angle difference between the FPM and FEM vector fields were lower than for the ventricular fiber arrangements and lower than for the atrial fiber arrangements. Discussion:The proposed meshless rule-based method was proven to generate myocardial fiber arrangements with very close agreement with FEM while alleviates the requirement for a mesh of the latter. This is of great value for cardiac electrophysiology solvers that are based on meshless methods since they require a well defined myocardial fiber arrangement to simulate accurately the propagation of electrical signals in the heart. Combining a meshless solution for both the determination of the fibers and the electrical signal propagation can allow for solution that do not require the definition of a mesh. To our knowledge, this work is the first one to propose a meshless rule-based method for myocardial fiber arrangement determination

Myocyte remodeling due to fibro-fatty infiltrations influences arrhythmogenicity

The onset of cardiac arrhythmias depends on the electrophysiological and structural properties of cardiac tissue. Electrophysiological remodeling of myocytes due to the presence of adipocytes constitutes a possibly important pathway in the pathogenesis of atrial fibrillation. In this paper we perform an in-silico study of the effect of such myocyte remodeling on the onset of atrial arrhythmias and study the dynamics of arrhythmia sources-spiral waves. We use the Courtemanche model for atrial myocytes and modify their electrophysiological properties based on published cellular electrophysiological measurements in myocytes co-cultered with adipocytes (a 69-87 % increase in APD90 and an increase of the RMP by 2.5-5.5 mV). In a generic 2D setup we show that adipose tissue remodeling substantially affects the spiral wave dynamics resulting in complex arrhythmia and such arrhythmia can be initiated under high frequency pacing if the size of the remodeled tissue is sufficiently large. These results are confirmed in simulations with an anatomically accurate model of the human atria

Parameter Estimation of Ion Current Formulations Requires Hybrid Optimization Approach to Be Both Accurate and Reliable

Computational models of cardiac electrophysiology provided insights into arrhythmogenesis and paved the way toward tailored therapies in the last years. To fully leverage in silico models in future research, these models need to be adapted to reflect pathologies, genetic alterations, or pharmacological effects, however. A common approach is to leave the structure of established models unaltered and estimate the values of a set of parameters. Today’s high-throughput patch clamp data acquisition methods require robust, unsupervised algorithms that estimate parameters both accurately and reliably. In this work, two classes of optimization approaches are evaluated: gradient-based trust-region-reflective and derivative-free particle swarm algorithms. Using synthetic input data and different ion current formulations from the Courtemanche et al. electrophysiological model of human atrial myocytes, we show that neither of the two schemes alone succeeds to meet all requirements. Sequential combination of the two algorithms did improve the performance to some extent but not satisfactorily. Thus, we propose a novel hybrid approach coupling the two algorithms in each iteration. This hybrid approach yielded very accurate estimates with minimal dependency on the initial guess using synthetic input data for which a ground truth parameter set exists. When applied to measured data, the hybrid approach yielded the best fit, again with minimal variation. Using the proposed algorithm, a single run is sufficient to estimate the parameters. The degree of superiority over the other investigated algorithms in terms of accuracy and robustness depended on the type of current. In contrast to the non-hybrid approaches, the proposed method proved to be optimal for data of arbitrary signal to noise ratio. The hybrid algorithm proposed in this work provides an important tool to integrate experimental data into computational models both accurately and robustly allowing to assess the often non-intuitive consequences of ion channel-level changes on higher levels of integration