Stochastic Finite Element Method for Torso Conductivity Uncertainties Quantification in Electrocardiography Inverse Problem
Mathematical modelling of natural phenomena, Tome 11 (2016) no. 2, pp. 1-19.

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The purpose of this paper is to study the influence of errors and uncertainties of the input data, like the conductivity, on the electrocardiography imaging (ECGI) solution. In order to do that, we propose a new stochastic optimal control formulation, permitting to calculate the distribution of the electric potentiel on the heart from the measurement on the body surface. The discretization is done using stochastic Galerkin method allowing to separate random and deterministic variables. Then, the problem is discretized, in spatial part, using the finite element method and the polynomial chaos expansion in the stochastic part of the problem. The considered problem is solved using a conjugate gradient method where the gradient of the cost function is computed with an adjoint technique. The efficiency of this approach to solve the inverse problem and the usability to quantify the effect of conductivity uncertainties in the torso are demonstrated through a number of numerical simulations on a 2D analytical geometry and on a 2D cross section of a real torso.
DOI : 10.1051/mmnp/201611201

R. Aboulaich 1 ; N. Fikal 1 ; E. El Guarmah 1, 2 ; N. Zemzemi 3, 4

1 Mohammed V University of Rabat, Mohammadia school of Engineering LERMA and LIRIMA Laboratories. Av. Ibn Sina Agdal, Rabat Morocco
2 Royal Air School, Informatics and Mathematics Department DFST, BEFRA, POB40002, Marrakech, Morocco
3 INRIA Bordeaux Sud-Ouest, Carmen project 200 rue de la vieille tour 33405 Talence Cedex, France
4 IHU Liryc, Electrophysiology and heart modeling institute. Avenue du Haut-Lévêque, 33604 Pessac, France
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R. Aboulaich; N. Fikal; E. El Guarmah; N. Zemzemi. Stochastic Finite Element Method for Torso Conductivity Uncertainties Quantification in Electrocardiography Inverse Problem. Mathematical modelling of natural phenomena, Tome 11 (2016) no. 2, pp. 1-19. doi : 10.1051/mmnp/201611201. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/201611201/

[1] R. Aboulaich, A. B. Abda, M. Kallel Inverse Problems and Imaging 2008 411 426

[2] S. Andrieux, T.N. Baranger, A.B. Abda Inverse problems 2006 115 133

[3] I. Babuska, R. Tempone, G.E. Zouraris Comput. Methods Appl. Mech. Engrg 2005 1251 1294

[4] I. Babuska, R. Tempone, G.E. Zouraris SIAM Journal on Numerical Analysis 2005 800 825

[5] G. Blatman, B. Sudret. An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probabilistic Engineering Mechanics, (2010), 183–197.

[6] M. Boulakia, S. Cazeau, M.A. Fernández, J.F. Gerbeau, N. Zemzemi Annals of biomedical engineering 2010 1071 1097

[7] P. Chen, A. Quarteroni, G. Rozza SIAM Journal on Numerical Analysis 2013 3163 3185

[8] O. Doessel, Y. Jiang, W.H. Schulze International Journal of Bioelectromagnetism 2011 178 183

[9] F.A. Duck. Physical Properties of Tissue: A Comprehensive Reference Book. London, England: Academic, Harcourt Brace Jovanovich, 1990.

[10] M. Eiermann, O.G. Ernst, E. Ullmann. Computational aspects of the stochastic finite element method. 10 (1) (2007), 3–15.

[11] T.J.C. Faes, D.M.H. Van, D.J.C. Munck, R.M. Heethaar Physiological measurement 1999 1 11

[12] M.A. Fernández, N. Zemzemi Mathematical biosciences 2010 58 75

[13] K.R. Foster, H.P. Schwan Critical reviews in biomedical engineering 1988 25 104

[14] S. Gabriel, R. Lau, C. Gabriel Physics in medicine and biology 1996 22 51

[15] S.E. Geneser, R.M. Kirby, R.S. Macleod Biomedical Engineering, IEEE Transactions on 2008 31 40

[16] R. Ghanem, P. Spanos. Stochastic Finite Elements:a Spectral Approach. Springer–Verlag, 1991.

[17] S. Ghosh, Y. Rudy Annals of Biomedical Engineering 2009 902 912

[18] J. Hadamard. Lectures on Cauchy’s problem in linear partial differential equations. Yale University Press, New Haven, 1923.

[19] P.C. Hansen, D.P. O'Leary SIAM Journal on Scientific Computing 1993 1487 1503

[20] L. S Hou, J. Lee, H. Manouzi Journal of Mathematical Analysis and Applications 2011 87 103

[21] O.P. Le Maître, M.T. Reagan, H.N. Najm, R.G. Ghanem, O.M. Knio Journal of computational Physics 2002 9 44

[22] V.A. Oosterom, G.J. Huiskamp Journal of electrocardiology 1989 53 72

[23] A. Rouatbi. Complétion de données via des méthodes de type controle. Master’s thesis, LAMSIN, E.N.I.T. Tunis, 2009.

[24] A.J. Shah, S. Yamashita, S. Zellerhoff, B. Berte, S. Mahida, D. Hooks, N. Aljefairi, N. Derval, A. Denis 2014

[25] F.M. Weber, D.U. Keller, S. Bauer, G. Seemann, C. Lorenz, O. Dossel Biomedical Engineering IEEE Transactions 2011 256 273

[26] S. Wiener Am. J. Math. 1998 897 936

[27] D. Xiu, G.E. Karniadakis Journal on Scientific Computing 2002 619 644

[28] E.V. Zakharov, A.V. Kalinin Computational Mathematics and Modeling 2009 247 257

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