The work developed in this thesis focusses on the electrical activity of the heart, from the modeling of the action potential originating from cardiac cells and propagating through the heart, as well as its electrical manifestation at the body surface. The study is divided in two main parts: modeling the action potential, and numerical simulations. For modeling the action potential a dimensional and asymptotic analysis is done. The key advance in this part of the work is that this analysis gives the steps to reliably control the action potential. It allows predicting the time/space scales and speed of any action potential that is to say the shape of the action potential and its propagation. This can be done as the explicit relations on all the physiological constants are defined precisely. This method facilitates the integrative modeling of a complete human heart with tissue-specific ionic models. It even proves that using a single model for the cardiac action potential is enough in many situations. For efficient numerical simulations, a numerical method for solving the heart-torso coupling problem is explored according to a level set description of the domains. This is done in the perspective of using directly medical images for building computational domains. A finite element method is then developed to manage meshes not adapted to internal interfaces. Finally, an anisotropic adaptive remeshing methods for unstructured finite element meshes is used to efficiently capture propagating action potentials within complex, realistic two dimensional geometries.

The work developed in this thesis focusses on the electrical activity of the heart, from the modeling of the action potential originating from cardiac cells and propagating through the heart, as well as its electrical manifestation at the body surface. The study is divided in two main parts: modeling the action potential, and numerical simulations. For modeling the action potential a dimensional and asymptotic analysis is done. The key advance in this part of the work is that this analysis gives the steps to reliably control the action potential. It allows predicting the time/space scales and speed of any action potential that is to say the shape of the action potential and its propagation. This can be done as the explicit relations on all the physiological constants are defined precisely. This method facilitates the integrative modeling of a complete human heart with tissue-specific ionic models. It even proves that using a single model for the cardiac action potential is enough in many situations. For efficient numerical simulations, a numerical method for solving the heart-torso coupling problem is explored according to a level set description of the domains. This is done in the perspective of using directly medical images for building computational domains. A finite element method is then developed to manage meshes not adapted to internal interfaces. Finally, an anisotropic adaptive remeshing methods for unstructured finite element meshes is used to efficiently capture propagating action potentials within complex, realistic two dimensional geometries.

The work developed in this thesis focusses on the electrical activity of the heart, from the modeling of the action potential originating from cardiac cells and propagating through the heart, as well as its electrical manifestation at the body surface. The study is divided in two main parts: modeling the action potential, and numerical simulations. For modeling the action potential a dimensional and asymptotic analysis is done. The key advance in this part of the work is that this analysis gives the steps to reliably control the action potential. It allows predicting the time/space scales and speed of any action potential that is to say the shape of the action potential and its propagation. This can be done as the explicit relations on all the physiological constants are defined precisely. This method facilitates the integrative modeling of a complete human heart with tissue-specific ionic models. It even proves that using a single model for the cardiac action potential is enough in many situations. For efficient numerical simulations, a numerical method for solving the heart-torso coupling problem is explored according to a level set description of the domains. This is done in the perspective of using directly medical images for building computational domains. A finite element method is then developed to manage meshes not adapted to internal interfaces. Finally, an anisotropic adaptive remeshing methods for unstructured finite element meshes is used to efficiently capture propagating action potentials within complex, realistic two dimensional geometries.

The work deals with the foundations of bioelectric phenomena cardiomyocyte, then it is also part of this description of the heart conduction system and method of distribution in this system The next section is a description of the spread in the system, the very essence of the spread. In the last chapter analyzes the theory for the creation of computational models, which extend in one dimension or two dimensions

The heart is one the most important organs in the human body and many other live creatures. The electrical activity in the heart controls the heart function, and many heart diseases are linked to the abnormalities in the electrical activity in the heart. Mathematical equations and computer simulation can be used to model the electrical activity in the heart. The heart models are challenging to solve because of the complexity of the models and the huge size of the problems. Several cell models have been proposed to model the electrical activity in a single heart cell. These models must be coupled with a heart model to model the electrical activity in the entire heart. The bidomain model is a popular model to simulate the propagation of electricity in myocardial tissue. It is a continuum-based model consisting of non-linear ordinary differential equations (ODEs) describing the electrical activity at the cellular scale and a system of partial differential equations (PDEs) describing propagation of electricity at the tissue scale. Because of this multi-scale, ODE/PDE structure of the model, splitting methods that treat the ODEs and PDEs in separate steps are natural candidates as numerical methods. First, we need to solve the problem at the cellular scale using ODE solvers. One of the most popular methods to solve the ODEs is known as the Rush-Larsen (RL) method. Its popularity stems from its improved stability over integrators such as the forward Euler (FE) method along with its easy implementation. The RL method partitions the ODEs into two sets: one for the gating variables, which are treated by an exponential integrator, and another for the remaining equations, which are treated by the FE method. The success of the RL method can be understood in terms of its relatively good stability when treating the gating variables. However, this feature would not be expected to be of benefit on cell models for which the stiffness is not captured by the gating equations. We demonstrate that this is indeed the case on a number of stiff cell models. We further propose a new partitioned method based on the combination of a first-order generalization of the RL method with the FE method. This new method leads to simulations of stiff cell models that are often one or two orders of magnitude faster than the original RL method. After solving the ODEs, we need to use bidomain solvers to solve the bidomain model. Two well-known, first-order time-integration methods for solving the bidomain model are the semi-implicit method and the Godunov operator-splitting method. Both methods decouple the numerical procedure at the cellular scale from that at the tissue scale but in slightly different ways. The methods are analyzed in terms of their accuracy, and their relative performance is compared on one-, two-, and three-dimensional test cases. As suggested by the analysis, the test cases show that the Godunov method is significantly faster than the semi-implicit method for the same level of accuracy, specifically, between 5 and 15 times in the cases presented. Second-order bidomain solvers can generally be expected to be more effective than first-order bidomain solvers under normal accuracy requirements. However, the simplest and the most commonly applied second-order method for the PDE step, the Crank-Nicolson (CN) method, may generate unphysical oscillations. We investigate the performance of a two-stage, L-stable singly diagonally implicit Runge-Kutta method for solving the PDEs of the bidomain model and present a stability analysis. Numerical experiments show that the enhanced stability property of this method leads to more physically realistic numerical simulations compared to both the CN and Backward Euler (BE) methods.

Sudden cardiac death following cardiac arrest is a major killer in the industrialised world. The leading cause of sudden cardiac death are disturbances in the normal electrical activation of cardiac tissue, known as cardiac arrhythmia, which severely compromise the ability of the heart to fulfill the body's demand of oxygen. Ventricular fibrillation (VF) is the most deadly form of cardiac arrhythmia. Furthermore, electrical defibrillation through the application of strong electric shocks to the heart is the only effective therapy against VF. Over the past decades, a large body of research has dealt with the study of the mechanisms underpinning the success or failure of defibrillation shocks. The main mechanism of shock failure involves shocks terminating VF but leaving the appropriate electrical substrate for new VF episodes to rapidly follow (i.e. shock-induced arrhythmogenesis). A large number of models have been developed for the in silico study of shock-induced arrhythmogenesis, ranging from single cell models to three-dimensional ventricular models of small mammalian species. However, no extrapolation of the results obtained in the aforementioned studies has been done in human models of ventricular electrophysiology. The main reason is the large computational requirements associated with the solution of the bidomain equations of cardiac electrophysiology over large anatomically-accurate geometrical models including representation of fibre orientation and transmembrane kinetics. In this Thesis we develop simulation technology for the study of cardiac defibrillation in the human heart in the framework of the open source simulation environment Chaste. The advances include the development of novel computational and numerical techniques for the solution of the bidomain equations in large-scale high performance computing resources. More specifically, we have considered the implementation of effective domain decomposition, the development of new numerical techniques for the reduction of communication in Chaste's finite element method (FEM) solver, and the development of mesh-independent preconditioners for the solution of the linear system arising from the FEM discretisation of the bidomain equations. The developments presented in this Thesis have brought Chaste to the level of performance and functionality required to perform bidomain simulations with large three-dimensional cardiac geometries made of tens of millions of nodes and including accurate representation of fibre orientation and membrane kinetics. This advances have enabled the in silico study of shock-induced arrhythmogenesis for the first time in the human heart, therefore bridging an important gap in the field of cardiac defibrillation research.

La modélisation du vivant, en particulier la modélisation de l'activité cardiaque, est devenue un défi scientifique majeur. Le but de cette thématique est de mieux comprendre les phénomènes physiologiques et donc d'apporter des solutions à des problèmes cliniques. Nous nous intéressons dans cette thèse à la modélisation et à l'étude numérique de l'activité électrique du cœur, en particulier l'étude des électrocardiogrammes (ECGs). L'onde électrique dans le cœur est gouvernée par un système d'équations de réaction-diffusion appelé modèle bidomaine ce système est couplé à une EDO représentant l'activité cellulaire. Afin simuler des ECGs, nous tenons en compte la propagation de l'onde électrique dans le thorax qui est décrite par une équation de diffusion. Nous commençons par une démonstrer l'existence d'une solution faible du système couplé cœur-thorax pour une classe de modèles ioniques phénoménologiques. Nous prouvons ensuite l'unicité de cette solution sous certaines conditions. Le plus grand apport de cette thèse est l'étude et la simulation numérique du couplage électrique cœur-thorax. Les résultats de simulations sont représentés à l'aide des ECGs. Dans une première partie, nous produisons des simulations pour un cas normal et pour des cas pathologiques (blocs de branche gauche et droit et des arhythmies). Nous étudions également l'impact de certaines hypothèses de modélisation sur les ECGs (couplage faible, utilisation du modèle monodomaine, isotropie, homogénéité cellulaire, comportement résistance-condensateur du péricarde,. . . ). Nous étudions à la fin de cette partie la sensibilité des ECGs par apport aux paramètres du modèle. En deuxième partie, nous effectuons l'analyse numérique de schémas du premier ordre en temps découplant les calculs du potentiel d'action et du potentiel extérieur. Puis, nous combinons ces schémas en temps avec un traîtement explicite du type Robin-Robin des conditions de couplage entre le cœur et le thorax. Nous proposons une analyse de stabilité de ces schémas et nous illustrons les résultats avec des simulations numériques d'ECGs. La dernière partie est consacrée à trois applications. Nous commençons par l'estimation de certains paramètres du modèle (conductivité du thorax et paramètres ioniques). Dans la deuxième application, qui est d'originie industrielle, nous utilisons des méthodes d'apprentissage statistique pour reconstruire des ECGs à partir de mesures ('électrogrammes). Enfin, nous présentons des simulations électro-mécaniques du coeur sur une géométrie réelle dans diverses situations physiologiques et pathologiques. Les indicateurs cliniques, électriques et mécaniques, calculés à partir de ces simulations sont très similaires à ceux observés en réalité.

Dans cette thèse, nous avons abordé deux problèmes de modélisation mathématique pour la propagation des signaux électriques cardiaques : la propagation à l’échelle tissulaire en présence d’hétérogénéités et la propagation à l’échelle cellulaire avec des jonctions communicantes non linéaires. Inclusions diffusives. Le modèle standard utilisé en électrocardiologie est le modèle bidomaine. Il est déduit par homogénéisation des propriétés microscopiques du tissu. Pour cela, on suppose que les myocytes électriquement actifs sont uniformément répartis dans le coeur. Bien que ce soit une hypothèse raisonnable pour des coeurs sains, ce n’est plus vrai dans certains cas pathologiques où des changements importants dans la structure tissulaire se produisent. C’est le cas, par exemple des maladies cardiaques ischémiques, rhumatismales et inflammatoires, de l’hypertrophie ou de l’infarctus. Ces hétérogénéités tissulaires sont souvent prises en compte à l’aide d’un ajustement ad hoc des paramètres du modèle. Le premier objectif de cette thèse consistait à généraliser les équations du modèle bidomaine au cas des pathologies cardiaques structurelles.Nous avons supposé une alternance périodique d’éléments de tissus sains (modèle bidomaine) et modifiées (inclusions diffusives). La simulation numérique directe d’un tel modèle nécessite une discrétisation très fine, et entraîne un coût de calcul élevé. Pour éviter cela, nous avons construit un modèle homogénéisé à l’échelle macroscopique en utilisant une analyse à deux échelles. Nous avons retrouvé un modèle de type bidomaine avec des coefficients de conductivité modifiés, dits effectifs. En complément, nous avons effectué une vérification numérique de la convergence du modèle microscopique vers celui homogénéisé, dans une situation bidimensionnelle.Dans la deuxième partie, nous avons quantifié les effets de différentes formes d’inclusions diffusives sur les coefficients de conductivité effectifs et leur anisotropie en 2D et 3D. De plus, nous avons effectué des simulations sur des domaines représentant des morceaux de tissu 2D avec ces coefficients de conductivité modifiés. Nous avons observé des changements de la vitesse de propagation et de la forme du front de l’onde de dépolarisation. Dans la troisième partie, nous avons simulé le modèle homogénéisé en 3D, à partir d’images par résonance magnétique (IRM) à haute résolution d’un coeur de rat. Nous avons évalué les propriétés structurelles du tissu en utilisant des outils d’analyse d’image.Nous avons ensuite utilisés ces évaluations pour construire les paramètres dans le modèle homogénéisé. Jonctions communicantes non linéaires. Dans la dernière partie de cette thèse, nous avons étudié les effets du comportement non linéaires des jonctions communicantes sur la propagation du signal à l’échelle cellulaire. Dans les modèles existants, les jonctions communicantes sont supposées avoir un comportement linéaire, lorsqu’elles sont modélisées.Cependant les données provenant des expériences montrent que ceux-ci ont un comportement non linéaire dépendant du temps et de la différence de potentiel entre cellules voisines. D’abord, nous avons présenté un modèle non linéaire 0D du courant dans les jonctions communicantes. Ensuite, nous avons recalé le modèle sur les données expérimentales.Enfin, nous avons proposé un modèle mathématique 2D qui décrit l’interaction électrique des myocytes cardiaques à l’échelle cellulaire. Ce modèle utilise le courant dans les jonctions communicantes comme une liaison directe entre des cellules adjacentes. / In this thesis we addressed two problems in mathematical modelling of propagation of electrical signals in the heart: tissue scale propagation with presence of tissue heterogeneities and cell scale propagation with non-linear gap junctions. Diffusive inclusions. The standard model used in cardiac electrophysiology is the bidomain model. It is an averaged model derived from the microscopic properties of the tissue.The bidomain model assumes that the electrically active myocytes are present uniformly everywhere in the heart. While this is a reasonable assumption for healthy hearts, it fails insome pathological cases where significant changes in the tissue structure occur, for examplein ischaemic and rheumatic heart disease, inflammation, hypertrophy, or infarction. These tissue heterogeneities are often taken into account through an ad-hoc tuning of model parameters. The first aim of this thesis consisted in generalizing the bidomain equations to the case of structural heart diseases.We assumed a periodic alternation of healthy (bidomain model) and altered (diffusive inclusion) tissue patches. Such a model may be simulated directly, at the high computational cost of a very fine discretisation. Instead we derived a homogenized model at the macroscopic scale, using a rigorous two-scale analysis. We recovered a bidomain-type model with modified conductivity coefficients, and performed a 2D numerical verificationof the convergence of the microscopic model towards the homogenized one.In the second part we quantified the effects of different shapes and sizes of diffusive inclusions on the effective conductivity coefficients and their anisotropy ratios in 2D and3D. Additionally, we ran simulations on 2D patches of tissue with modified conductivity coefficients. We observed changes in the propagation velocity as well as in the shape of the depolarization wave-front.In the third part, based on high-resolution MR images of a rat heart we simulated 3D propagations with the homogenized model. Using image analysis software tools we assessed the structural properties of the tissue, that we used afterwards as parameters inthe homogenized model. Non-linear gap junctions. In the last part of this thesis, we studied the effects of nonlineargap junction channels on the signal propagation at the cell scale. In existing models, the gap junction channels, if modelled, are assumed to have a linear behaviour, while from experimental data we know that they have a time- and voltage-dependent non-linear behaviour. Firstly, we stated a non-linear 0D model for the gap junctional current, and secondly fitted the model to available experimental data. Finally, we proposed a 2D mathematical model that describes the electrical interaction of cardiac myocytes on the cell scale. It accounts for the gap junctional current as "the direct link" between the adjacent cells.