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Fast Data-Driven Calibration of a Cardiac Electrophysiology Model from Images and ECG

  • Oliver Zettinig
  • Tommaso Mansi
  • Bogdan Georgescu
  • Elham Kayvanpour
  • Farbod Sedaghat-Hamedani
  • Ali Amr
  • Jan Haas
  • Henning Steen
  • Benjamin Meder
  • Hugo Katus
  • Nassir Navab
  • Ali Kamen
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)

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.

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.

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References

  1. 1.
    Marcus, G.M., Keung, E., Scheinman, M.M.: The year in review of cardiac electrophysiology. JACC 61(7), 772–782 (2013)CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Chhay, M., Coudière, Y., Turpault, R.: How to compute the extracellular potential in electrocardiology from an extended monodomain model. RR-7916, INRIA (2012)Google Scholar
  13. 13.
    Mitchell, C., Schaeffer, D.: A two-current model for the dynamics of cardiac membrane. Bull. Math. Biol. 65(5), 767–793 (2003)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Oliver Zettinig
    • 1
    • 2
  • Tommaso Mansi
    • 1
  • Bogdan Georgescu
    • 1
  • Elham Kayvanpour
    • 3
  • Farbod Sedaghat-Hamedani
    • 3
  • Ali Amr
    • 3
  • Jan Haas
    • 3
  • Henning Steen
    • 3
  • Benjamin Meder
    • 3
  • Hugo Katus
    • 3
  • Nassir Navab
    • 2
  • Ali Kamen
    • 1
  • Dorin Comaniciu
    • 1
  1. 1.Corporate Technology, Imaging and Computer VisionSiemens CorporationPrincetonUSA
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  3. 3.Department of Internal Medicine III - Cardiology, Angiology and PneumologyUniversity Hospital HeidelbergHeidelbergGermany

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