Biomedical Engineering Reference
In-Depth Information
and M. Sermesant (eds,), Functional Imaging and Modeling of the Heart, volume 5528 of
Lecture Notes in Computer Science, pp. 513-523. Springer, 2009.
[17] Colli Franzone P., Pavarino L.F., Taccardi B.: Simulating patterns of excitation, repolariza-
tion and action potential duration with cardiac bidomain and monodomain models. Math.
Biosci.
197
(1): 35-66, 2005.
[18] Colli Franzone P., Savare G.: Degenerate evolution systems modeling the cardiac electric
field at micro- and macroscopic level. In Evolution equations, semigroups and functional
analysis (Milano, 2000), volume 50 of Progr. Nonlinear Differential Equations Appl., pp. 49-
78. Birkhauser, Basel, 2002.
[19] Djabella K., Sorine M.: Differential model of the excitation-contraction coupling in a cardiac
cell for multicycle simulations. In EMBEC'05, volume 11, pp. 4185-4190, Prague, 2005.
[20] Dumas L., El Alaoui L.: How genetic algorithms can improve a pacemaker efficiency. In
GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evo-
lutionary computation, pp. 2681-2686, New York, NY, USA, 2007. ACM.
[21] Ebrard G., Fernandez M.A., Gerbeau J.-F., Rossi F., Zemzemi N.: From intracardiac elec-
trograms to electrocardiograms. models and metamodels. In N. Ayache, H. Delingette, and
M. Sermesant (eds.), Functional Imaging and Modeling of the Heart, volume 5528 of Lecture
Notes in Computer Science, pp. 524-533. Springer, 2009.
[22] Efimov I.R., Gray R.A., Roth B.J.: Virtual electrodes and deexcitation: new insights into
fibrillation induction and defibrillation. J. Cardiovasc. Electrophysiol.
11
(3): 339-353, 2000.
[23] Ethier M., Bourgault Y.: Semi-implicit time-discretization schemes for the bidomain model.
SIAM J. Numer. Anal.
46
(5): 2443-2468, 2008.
[24] Fenton F., Karma A.: Vortex dynamics in three-dimensional continuous myocardium with
fiber rotation: Filament instability and fibrillation. Chaos
8
(1): 20-47, 1998.
[25] Fernandez M.A., Zemzemi N.: Decoupled time-marching schemes in computational cardiac
electrophysiology and ECG numerical simulation. Math. Biosci.
226
(1): 58-75, 2010.
[26] FitzHugh R.: Impulses and physiological states in theoretical models of nerve membrane.
Biophys. J.
1
: 445-465, 1961.
[27] Goldberger A.L.: Clinical Electrocardiography: A Simplified Approach. Mosby-Elsevier, 7th
edition, 2006.
[28] Gulrajani R.M.: Models of the electrical activity of the heart and computer simulation of the
electrocardiogram. Crit. Rev. Biomed. Eng.
16
(1): 1-6, 1988.
[29] Hooke N., Henriquez C.S., Lanzkron P., Rose D.: Linear algebraic transformations of the
bidomain equations: implications for numerical methods. Math. Biosci.
120
(2): 127-145,
1994.
[30] Huiskamp G.: Simulation of depolarization in a membrane-equations-based model of the
anisotropic ventricle. IEEE Trans. Biomed. Eng.
5045
(7): 847-855, 1998.
[31] Keener J. P., Bogar K.: A numerical method for the solution of the bidomain equations in
cardiac tissue. Chaos
8
(1): 234-241, 1998.
[32] Keldermann R.H., Nash M.P., Panfilov A.V.: Modeling cardiac mechano-electrical feedback
using reaction-diffusion-mechanics systems. Physica D: Nonlinear Phenomena
238
(11-12):
1000-1007, 2009.
[33] Keller D.U.J., Seemann G., Weiss D.L., Farina D., Zehelein J., Dossel O.: Computer based
modeling of the congenital long-qt 2 syndrome in the visible man torso: From genes to ECG.
In Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp. 1410-
1413, 2007.
[34] Kerckhoffs R.C.P., Healy S.N., Usyk T.P., McCulloch A.D.: Computational methods for car-
diac electromechanics. Proc. IEEE
94
(4): 769-783, 2006.
[35] Krassowska W., Neu J.C.: Effective boundary conditions for syncitial tissues. IEEE Trans.
Biomed. Eng.
4
1(2): 143-150, 1994.
[36] Kunisch K., Volkwein S.: Galerkin proper orthogonal decomposition methods for parabolic
problems. Numerische Mathematik
90
(1): 117-148, 2001.
[37] Lab M.J., Taggart P., Sachs F.: Mechano-electric feedback. Cardiovasc. Res.
32
: 1-2, 1996.
