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From Intracardiac Electrograms to Electrocardiograms: Models and Metamodels

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Functional Imaging and Modeling of the Heart (FIMH 2009)

Abstract

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.

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References

  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)

    Chapter  Google Scholar 

  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/abn002

    Google Scholar 

  3. Malmivuo, J., Plonsey, R.: Bioelectromagnetism. Principles and applications of bioelectric and biomagnetic fields. Oxford University Press, New York (1995)

    Book  Google Scholar 

  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. Nagumo, J.S., Arimoto, S., Yoshizawa, S.: An active pulse transmission line stimulating nerve axon. Proc. IRE (50), 2061–2071 (1962)

    Google Scholar 

  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. Sachse, F.B.: Computational Cardiology: Modeling of Anatomy, Electrophysiology, and Mechanics. Springer, Heidelberg (2004)

    Book  MATH  Google Scholar 

  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. Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)

    Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  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)

    MATH  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Ebrard, G., Fernández, M.A., Gerbeau, JF., Rossi, F., Zemzemi, N. (2009). From Intracardiac Electrograms to Electrocardiograms: Models and Metamodels. In: Ayache, N., Delingette, H., Sermesant, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2009. Lecture Notes in Computer Science, vol 5528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01932-6_56

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  • DOI: https://doi.org/10.1007/978-3-642-01932-6_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01931-9

  • Online ISBN: 978-3-642-01932-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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