From Intracardiac Electrograms to Electrocardiograms: Models and Metamodels

  • Géraldine Ebrard
  • Miguel A. Fernández
  • Jean-Frédéric Gerbeau
  • Fabrice Rossi
  • Nejib Zemzemi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5528)


We consider the problem of building a standard electrocardiogram (ECG) from the electrical potential provided by a pacemaker in a few points of the heart (electrogram). We use a 3D mathematical model of the heart and the torso electrical activity, able to generate “computational ECG”, and a “metamodel” based on a kernel ridge regression. The input of the metamodel is the electrogram, its output is the ECG. The 3D model is used to train and test the metamodel. We illustrate the performance of the proposed strategy on simulated bundle branch blocks of various severities and a few clinical data.


Support Vector Machine Support Vector Regression Reproduce Kernel Hilbert Space Right Bundle Branch Block Support Vector Regression Model 
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  1. 1.
    Boulakia, M., Fernández, M.A., Gerbeau, J.-F., Zemzemi, N.: Towards the numerical simulation of electrocardiograms. In: Sachse, F.B., Seemann, G. (eds.) FIMH 2007. LNCS, vol. 4466, pp. 240–249. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Boulakia, M., Fernández, M.A., Gerbeau, J.-F., Zemzemi, N.: A coupled system of PDEs and ODEs arising in electrocardiograms modelling. Applied Math. Res. Exp. (2008), doi:10.1093/amrx/abn002Google Scholar
  3. 3.
    Malmivuo, J., Plonsey, R.: Bioelectromagnetism. Principles and applications of bioelectric and biomagnetic fields. Oxford University Press, New York (1995)CrossRefGoogle Scholar
  4. 4.
    Mitchell, C.C., Schaeffer, D.G.: A two-current model for the dynamics of cardiac membrane. Bulletin Math. Bio. (65), 767–793 (2003)Google Scholar
  5. 5.
    Nagumo, J.S., Arimoto, S., Yoshizawa, S.: An active pulse transmission line stimulating nerve axon. Proc. IRE (50), 2061–2071 (1962)Google Scholar
  6. 6.
    Opper, M., Winther, O.: Advances in Large-Margin Classifiers. In: Gaussian Processes and SVM: Mean Field and Leave-One-Out, ch. 17. MIT Press, Cambridge (2000)Google Scholar
  7. 7.
    Sachse, F.B.: Computational Cardiology: Modeling of Anatomy, Electrophysiology, and Mechanics. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar
  8. 8.
    Saunders, C., Gammerman, A., Vovk, V.: Ridge regression learning algorithm in dual variables. In: Proceedings of the Fifteenth International Conference on Machine Learning (ICML1998), Madison, Wisconsin, USA, July 1998, pp. 515–521 (1998)Google Scholar
  9. 9.
    Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)Google Scholar
  10. 10.
    Steinwart, I.: On the influence of the kernel on the consistency of support vector machines. Journal of Machine Learning Research 2, 67–93 (2001)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Sundnes, J., Lines, G.T., Cai, X., Nielsen, B.F., Mardal, K.-A., Tveito, A.: Computing the electrical activity in the heart. Springer, Heidelberg (2006)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Géraldine Ebrard
    • 1
  • Miguel A. Fernández
    • 1
  • Jean-Frédéric Gerbeau
    • 1
  • Fabrice Rossi
    • 2
  • Nejib Zemzemi
    • 1
    • 3
  1. 1.INRIA ParisLe Chesnay CedexFrance
  2. 2.Institut TELECOMTELECOM ParisTech, LTCI – UMR CNRS 5141ParisFrance
  3. 3.Laboratoire de mathématiques d’OrsayUniversité Paris 11Orsay CedexFrance

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