Simplified 2D Bidomain Model of Whole Heart Electrical Activity and ECG Generation

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Abstract

The aim of this study was the development of a geometrically simple and highly computationally-efficient two dimensional (2D) biophysical model of whole heart electrical activity, incorporating spontaneous activation of the sinoatrial node (SAN), the specialized conduction system, and realistic surface ECG morphology computed on the torso. The FitzHugh-Nagumo (FHN) equations were incorporated into a bidomain finite element model of cardiac electrical activity, which was comprised of a simplified geometry of the whole heart with the blood cavities, the lungs and the torso as an extracellular volume conductor. To model the ECG, we placed four electrodes on the surface of the torso to simulate three Einthoven leads VI, VII and VIII from the standard 12-lead system. The 2D model was able to reconstruct ECG morphology on the torso from action potentials generated at various regions of the heart, including the sinoatrial node, atria, atrioventricular node, His bundle, bundle branches, Purkinje fibers, and ventricles. Our 2D cardiac model offers a good compromise between computational load and model complexity, and can be used as a first step towards three dimensional (3D) ECG models with more complex, precise and accurate geometry of anatomical structures, to investigate the effect of various cardiac electrophysiological parameters on ECG morphology.

[1] Pullan, A.J., Buist, M.L., Cheng, L.K. (2005). Mathematically Modelling the Electrical Activity of the Heart - From Cell to Body Surface and Back Again. World Scientific.

[2] Seemann, G. et al. (2010). Electrophysiological modeling for cardiology: Methods and potential applications. it - Information Technology, 52 (5), 242-249.

[3] Trayanova, N.A. (2011). Whole-heart modeling - Applications to cardiac electrophysiology and electromechanics. Circulation Research, 108, 113-128.

[4] Trudel, M.-C. et al. (2004). Simulation of QRST integral maps with a membrane-based computer heart model employing parallel processing. IEEE Transactions of Biomedical Engineering, 51 (8), 1319-1329.

[5] Jacquemet, V., van Oosterom, A., Vesin, J., Kappenberger, L. (2006). Analysis of electrocardiograms during atrial fibrillation. IEEE Engineering in Medicine and Biology Magazine, 25 (6), 79-88.

[6] Pfeifer, B. et al. (2007). A training whole-heart model for simulating propagation and ECG patters. Biomedical Signal Processing and Control, 2 (4), 323-330.

[7] Van Oosterom, A., Oostendorp, T.F. (2003). ECGSIM: An interactive tool for studying the genesis of QRST waveforms. Heart, 90 (2), 165-168.

[8] Garny, A., Noble, D., Kohl, P. (2005). Dimensionality in cardiac modelling. Progress in Biophysics and Molecular Biology, 87 (1), 47-66.

[9] Sovilj, S., Magjarevic, R., Lovell, N., Dokos, S. (2013). Realistic 3D bidomain model of whole heart electrical activity and ECG generation. In Computing in Cardiology Conference, 22-25 September 2013. IEEE, 377-380.

[10] Sovilj, S., Magjarevic, R., Lovell, N., Dokos, S. (2013). A simplified 3D model of whole heart electrical activity and 12-lead ECG generation. Computational and Mathematical Methods in Medicine, article ID 134208-10.

[11] Gibbons Kroeker, C.A., Adeeb, S., Tyberg, J.V., Shrive, N.G. (2006). A 2D FE model of the heart demonstrates the role of the pericardium in ventricular deformation. American Journal of Physiology - Heart and Circulatory Physiology, 291 (5), H2229-2236.

[12] Gabriel, S., Lau, R.W., Gabriel, C. (1996). The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Physics in Medicine and Biology, 41, 2251-2269.

[13] Malmivuo, J., Plonsey, R. (1995). Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. Oxford University Press.

[14] Fenton, F.H., Cherry, E.M. (2008). Models of cardiac cell. Scholarpedia, 3 (8), 1868.

[15] Dokos, S., Cloherty, S.L., Lovell, N.H. (2007). Computational model of atrial electrical activation and propagation. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 908-911.

[16] Rogers, J.M., McCulloch, A.D. (1994). A collocation- Galerkin finite element model of cardiac action potential propagation. IEEE Transactions on Biomedical Engineering, 41, 743-757.

[17] Petra, N., Gobbert, K.M. (2009). Parallel performance studies for COMSOL multiphysics using scripting and batch processing. In Proceedings of the COMSOL Conference 2009 Boston.

[18] Janse, M.J. (1997). Why does atrial fibrillation occur? European Heart Journal, 18, C12-C18.

[19] Nattel, S. (2002). New ideas about atrial fibrillation 50 years on. Nature, 415, 219-226.

[20] Seed, W.A. et al. (1987). Relation of human cardiac action potential duration to the interval between beats: Implications for the validity of rate corrected QT interval (QTc). British Heart Journal, 57, 32-37.

[21] Sovilj, S., Rajsman, G., Magjarevic, R. (2011). ECG based prediction of atrial fibrillation using support vector classifier. Automatika - Journal for Control, Measurement, Electronics, Computing and Communications, 52 (1), 58-67.

[22] Sovilj, S., Van Oosterom, A., Rajsman, G., Magjarevic, R. (2010). ECG based prediction of atrial fibrillation development following coronary artery bypass grafting. Physiological Measurement, 31, 663-677.

Measurement Science Review

The Journal of Institute of Measurement Science of Slovak Academy of Sciences

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IMPACT FACTOR 2017: 1.345
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CiteScore 2016: 1.88

SCImago Journal Rank (SJR) 2016: 0.495
Source Normalized Impact per Paper (SNIP) 2016: 1.419

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