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Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology

  • Sophie Giffard-RoisinEmail author
  • Hervé Delingette
  • Thomas Jackson
  • Lauren Fovargue
  • Jack Lee
  • Aldo Rinaldi
  • Nicholas Ayache
  • Reza Razavi
  • Maxime SermesantEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10263)

Abstract

In the scope of modelling cardiac electrophysiology (EP) for understanding pathologies and predicting the response to therapies, patient-specific model parameters need to be estimated. Although personalisation from non-invasive data (body surface potential mapping, BSPM) has been investigated on simple cases mostly with a single pacing site, there is a need for a method able to handle more complex situations such as sinus rhythm with several onsets. In the scope of estimating cardiac activation maps, we propose a sparse Bayesian kernel-based regression (relevance vector machine, RVM) from a large patient-specific simulated database. RVM additionally provides a confidence on the result and an automatic selection of relevant features. With the use of specific BSPM descriptors and a reduced space for the myocardial geometry, we detail this framework on a real case of simultaneous biventricular pacing where both onsets were precisely localised. The obtained results (mean distance to the two ground truth pacing leads is 18.4 mm) demonstrate the usefulness of this non-linear approach.

Keywords

ECG imaging Personalisation Relevance Vector Machine Cardiac electrophysiology 

Notes

Acknowledgments

The research leading to these results has received funding from the Seventh Framework Programme (FP7/2007-2013) under grant agreement VP2HF n\(^{\circ }\)611823.

References

  1. 1.
    Sermesant, M., Chabiniok, R., Chinchapatnam, P., et al.: Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in crt: A preliminary clinical validation. Med. Image Anal. 16(1), 201–215 (2012)CrossRefGoogle Scholar
  2. 2.
    Giffard-Roisin, S., Jackson, T., Fovargue, L., Lee, J., Delingette, H., Razavi, R., Ayache, N., Sermesant, M.: Non-invasive personalisation of a cardiac electrophysiology model from body surface potential mapping. IEEE Trans. Biomed. Eng. (2016). doi: 10.1109/TBME.2016.2629849
  3. 3.
    Dhamala, J., Sapp, J.L., Horacek, M., Wang, L.: Spatially-adaptive multi-scale optimization for local parameter estimation: application in cardiac electrophysiological models. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 282–290. Springer, Cham (2016). doi: 10.1007/978-3-319-46726-9_33 CrossRefGoogle Scholar
  4. 4.
    Reuter, M., Wolter, F., Peinecke, N.: Laplace-spectra as fingerprints for shape matching. In: ACM Symposium on Solid and Physical Modeling (2005)Google Scholar
  5. 5.
    Umeyama, S.: An eigendecomposition approach to weighted graph matching problems. IEEE Trans. Pattern Anal. Mach. Intell. 10(5), 695–703 (1988)CrossRefzbMATHGoogle Scholar
  6. 6.
    Tipping, M.E., Faul, A.C.: Fast marginal likelihood maximisation for sparse Bayesian models. In: AISTATS (2003)Google Scholar
  7. 7.
    He, B., Li, G., Zhang, X.: Noninvasive three-dimensional activation time imaging of ventricular excitation by means of a heart-excitation model. Phys. Med. Biol. 47(22), 4063 (2002)CrossRefGoogle Scholar
  8. 8.
    Soto-Iglesias, D., Butakoff, C., Andreu, D., Fernández-Armenta, J., Berruezo, A., Camara, O.: Integration of electro-anatomical and imaging data of the left ventricle: an evaluation framework. Med. Image Anal. 32, 131–144 (2016)CrossRefGoogle Scholar
  9. 9.
    Le Folgoc, L., Delingette, H., Criminisi, A., Ayache, N.: Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements. Med. Image Anal. 36, 79–97 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sophie Giffard-Roisin
    • 1
    Email author
  • Hervé Delingette
    • 1
  • Thomas Jackson
    • 2
  • Lauren Fovargue
    • 2
  • Jack Lee
    • 2
  • Aldo Rinaldi
    • 2
  • Nicholas Ayache
    • 1
  • Reza Razavi
    • 2
  • Maxime Sermesant
    • 1
    Email author
  1. 1.Université Côte d’Azur, InriaNiceFrance
  2. 2.Department of Biomedical EngineeringKing’s College LondonLondonUK

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