Electrical Activity in the Human Heart

Functional Imaging and Modeling of the Heart

2011

Abstract. We consider the problem of estimating some parameters (like ionic models or parameters involved in the initial stimulation) of a model of electrocardiograms (ECG) from the data of the Einthoven leads. This problem can be viewed as a first attempt to identify or to locate a pathology. The direct model is based on the bidomain equations in the heart and a Poisson equation in the torso and. To keep the computational time reasonable, the evaluation of the direct problem is approximated with a reduced order model based on Proper Orthogonal Decomposition (POD). The optimization problem is solved using a genetic algorithm. Numerical tests show that, with noisy synthetic data, the proposed procedure allows to recover ionic parameters and initial activation regions with a fair accuracy.

Section: Resolution Of the Direct Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter Identification in Cardiac Electrophysiology Using Proper Orthogonal Decomposition Method

Boulakia

Functional Imaging and Modeling of the Heart

2011

“…In order to describe the electrical potential in the heart, we use the standard nonlinear reaction-diffusion equations known as the bidomain equations [55,58]. In terms of extracellular potential u e and transmembrane potential V m = u i − u e , with u i the intracellular potential, the bidomain model reads…”

Section: Bidomain Modelmentioning

confidence: 99%

“…But our simulator can of course provide more sophisticated measurements, like those obtained with electrode vests. Many studies have been carried out on this topic: on the forward problem and the analysis of the number of electrodes [28,25], or on the reconstruction of the potential on the heart surface [53,59]. Our objective here is less ambitious: we just show an example of a statistical analysis that can be done with the measures provided by our ECG simulator.…”

Section: Comparison With the Mitchell-schaeffer Modelmentioning

confidence: 99%

Numerical simulation of electrocardiograms for full cardiac cycles in healthy and pathological conditions

Schenone

Collin

Numer Methods Biomed Eng

2015

This work is dedicated to the simulation of full cycles of the electrical activity of the heart and the corresponding body surface potential. The model is based on a realistic torso and heart anatomy, including ventricles and atria. One of the specificities of our approach is to model the atria as a surface, which is the kind of data typically provided by medical imaging for thin volumes. The bidomain equations are considered in their usual formulation in the ventricles, and in a surface formulation on the atria. Two ionic models are used: the Courtemanche-Ramirez-Nattel model on the atria, and the "Minimal model for human Ventricular action potentials" (MV) by Bueno-Orovio, Cherry and Fenton in the ventricles. The heart is weakly coupled to the torso by a Robin boundary condition based on a resistorcapacitor transmission condition. Various ECGs are simulated in healthy and pathological conditions (left and right bundle branch blocks, Bachmann's bundle block, Wolff-ParkinsonWhite syndrome). To assess the numerical ECGs, we use several qualitative and quantitative criteria found in the medical literature. Our simulator can also be used to generate the signals measured by a vest of electrodes. This capability is illustrated at the end of the article.

“…The model consists of two degenerate parabolic reaction-diffusion partial differential equations (PDE), known as the bidomain equations (see e.g. [7,11]), coupled to a system of ordinary differential equations (ODE):…”

Section: Modelmentioning

confidence: 99%

From Intracardiac Electrograms to Electrocardiograms: Models and Metamodels

Ebrard

Fernández

Functional Imaging and Modeling of the Heart

et al. 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.