Abstract
Recent advances in computational electrophysiology (EP) models make them attractive for clinical use. We propose a novel data-driven approach to calibrate an EP model from standard 12-lead electrocardiograms (ECG), which are in contrast to invasive or dense body surface measurements widely available in clinical routine. With focus on cardiac depolarization, we first propose an efficient forward model of ECG by coupling a mono-domain, Lattice-Boltzmann model of cardiac EP to a boundary element formulation of body surface potentials. We then estimate a polynomial regression to predict myocardium, left ventricle and right ventricle endocardium electrical diffusion from QRS duration and ECG electrical axis. Training was performed on 4,200 ECG simulations, calculated in ≈3s each, using different diffusion parameters on 13 patient geometries. This allowed quantifying diffusion uncertainty for given ECG parameters due to the ill-posed nature of the ECG problem. We show that our method is able to predict myocardium diffusion within the uncertainty range, yielding a prediction error of less than 5ms for QRS duration and 2° for electrical axis. Prediction results compared favorably with those obtained with a standard optimization procedure, while being 60 times faster. Our data-driven model can thus constitute an efficient preliminary step prior to more refined EP personalization.
Chapter PDF
Similar content being viewed by others
Keywords
- Forward Model
- Left Bundle Branch Block
- Electrical Axis
- Body Surface Mapping Potential
- Total Standard Deviation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Marcus, G.M., Keung, E., Scheinman, M.M.: The year in review of cardiac electrophysiology. JACC 61(7), 772–782 (2013)
Clayton, R.H., Bernus, O., Cherry, E.M., Dierckx, H., Fenton, F.H., Mirabella, L., Panfilov, A.V., Sachse, F.B., Seemann, G., Zhang, H.: Models of cardiac tissue electrophysiology: Progress, challenges and open questions. PBMB 104(1), 22–48 (2011)
Rapaka, S., Mansi, T., Georgescu, B., Pop, M., Wright, G.A., Kamen, A., Comaniciu, D.: LBM-EP: Lattice-Boltzmann method for fast cardiac electrophysiology simulation from 3D images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 33–40. Springer, Heidelberg (2012)
Talbot, H., Marchesseau, S., Duriez, C., Sermesant, M., Cotin, S., Delingette, H.: Towards an interactive electromechanical model of the heart. Int. Focus 3(2) (2013)
Relan, J., Chinchapatnam, P., Sermesant, M., Rhode, K., Ginks, M., Delingette, H., Rinaldi, C.A., Razavi, R., Ayache, N.: Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia. Int. Focus 1(3), 396–407 (2011)
Dössel, O., Krueger, M., Weber, F., Schilling, C., Schulze, W., Seemann, G.: A framework for personalization of computational models of the human atria. In: IEEE Proc. EMBC 2011, pp. 4324–4328 (2011)
Wang, L., Wong, K.C., Zhang, H., Liu, H., Shi, P.: Noninvasive computational imaging of cardiac electrophysiology for 3-d infarct. IEEE TBE 58(4), 1033 (2011)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE TMI 27(11), 1668–1681 (2008)
Konukoglu, E., Relan, J., Cilingir, U., Menze, B.H., Chinchapatnam, P., Jadidi, A., Cochet, H., Hocini, M., Delingette, H., Jaïs, P., Haïssaguerre, M., Ayache, N., Sermesant, M.: Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to Eikonal-diffusion models in cardiac electrophysiology. PBMB 107(1), 134–146 (2011)
Jiang, M., Lv, J., Wang, C., Huang, W., Xia, L., Shou, G.: A hybrid model of maximum margin clustering method and support vector regression for solving the inverse ECG problem. Computing in Cardiology 2011, 457–460 (2011)
Boulakia, M., Cazeau, S., Fernández, M.A., Gerbeau, J.-F., Zemzemi, N.: Mathematical modeling of electrocardiograms: a numerical study. Ann. Biomed. Eng. 38(3), 1071–1097 (2010)
Chhay, M., Coudière, Y., Turpault, R.: How to compute the extracellular potential in electrocardiology from an extended monodomain model. RR-7916, INRIA (2012)
Mitchell, C., Schaeffer, D.: A two-current model for the dynamics of cardiac membrane. Bull. Math. Biol. 65(5), 767–793 (2003)
Shou, G., Xia, L., Jiang, M., Wei, Q., Liu, F., Crozier, S.: Solving the ECG forward problem by means of standard H- and H-hierarchical adaptive linear boundary element method. IEEE TBE 56(5), 1454–1464 (2009)
Kohler, B.U., Hennig, C., Orglmeister, R.: The principles of software QRS detection. IEEE Engineering in Medicine and Biology Magazine 21(1), 42–57 (2002)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zettinig, O. et al. (2013). Fast Data-Driven Calibration of a Cardiac Electrophysiology Model from Images and ECG. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-40811-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40810-6
Online ISBN: 978-3-642-40811-3
eBook Packages: Computer ScienceComputer Science (R0)