A Brief History of Tissue Models For Cardiac Electrophysiology

Henriquez, Craig S.

doi:10.1109/tbme.2014.2310515

Cited by 43 publications

(38 citation statements)

References 102 publications

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“…From z = 1, extending to infinity in the positive z direction there is assumed to be a volume of blood that is in contact with the endocardium. The ventricular tissue is approximated using the bidomain model [1,[24][25][26]. This regards the tissue as consisting of extracellular (e) and intracellular (i) interpenetrating domains, over which the cardiac properties are averaged, thus leading to six bidomain cardiac conductivity values g el , g et , g en , g il , g it , g in being required in the model.…”

Section: Model Geometrymentioning

confidence: 99%

“…Along with these advances, has come the realisation of the importance of accurate values for the parameters in these models. For example, this is the case for the conductivity values used in the bidomain model that is commonly used to model cardiac tissue [1,2]. In addition, not only is it essential to find conductivity values that represent normal tissue, it is also necessary to understand how these are affected in diseased and damaged tissue [1,3].…”

Section: Introductionmentioning

confidence: 99%

“…For example, this is the case for the conductivity values used in the bidomain model that is commonly used to model cardiac tissue [1,2]. In addition, not only is it essential to find conductivity values that represent normal tissue, it is also necessary to understand how these are affected in diseased and damaged tissue [1,3]. Despite the bidomain model being used for modelling electrophysiological phenomena for nearly fifty years [1], only three sets of experimentally determined bidomain conductivities exist [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, not only is it essential to find conductivity values that represent normal tissue, it is also necessary to understand how these are affected in diseased and damaged tissue [1,3]. Despite the bidomain model being used for modelling electrophysiological phenomena for nearly fifty years [1], only three sets of experimentally determined bidomain conductivities exist [4][5][6]. Unfortunately, these values are inconsistent [7] and can lead to very different results in simulation studies [8].…”

Section: Introductionmentioning

confidence: 99%

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Determining six cardiac conductivities from realistically large datasets

Johnston

2015

Mathematical Biosciences

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Simulation studies of cardiac electrophysiological behaviour that use the bidomain model require accurate values for the bidomain extracellular and intracellular conductivities to produce useful results. This work considers an inversion algorithm, which has previously been shown, using simulated data, to be capable of retrieving six bidomain conductivities and the fibre rotation angle from measurements of electric potential made in the heart. The aim here is to see whether it is possible to improve the accuracy of the retrieved parameters. The scenario of retrieving only conductivities and not fibre rotation is examined but this does not lead to a worthwhile improvement in retrieval accuracy. It is also found that it is possible to retrieve the bidomain conductivities using not two but just one pass of the algorithm, made on a 'widely-spaced' electrode set. This appears to work because the algorithm is still very sensitive to the extracellular conductivities. However, the single-pass method is not recommended because the intracellular conductivities that are retrieved are not as accurate as those that are retrieved in the usual two-pass method, particularly for higher values of added noise. The second part of this work considers retrieving the six conductivities and fibre rotation from realistically large sets of potential measurements and identifies the best data analysis method. It is found that, even with added noise of up to 40%, the extracellular conductivities can still be retrieved extremely accurately (relative errors of around 2% on average) and so can the intracellular longitudinal conductivities and fibre rotation (errors less than 8% on average). The remaining intracellular conductivities have errors that are generally less than twice the added noise, particularly for the higher noise values.

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Section: Model Geometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determining six cardiac conductivities from realistically large datasets

Johnston

2015

Mathematical Biosciences

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“…The electric activity of the heart is usually studied using the well known bidomain model [4,5]. It consists of an elliptic partial differential equation and a parabolic partial differential equation coupled to a system of stiff nonlinear ordinary differential equations describing the ionic current through the cellular membrane.…”

Section: Introductionmentioning

confidence: 99%

GPU accelerated solver for nonlinear reaction–diffusion systems. Application to the electrophysiology problem

Mena

Ferrero

Matas

2015

Computer Physics Communications

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Solving the electric activity of the heart posses a big challenge, not only because of the structural complexities inherent to the heart tissue, but also because of the complex electric behaviour of the cardiac cells. The multiscale nature of the electrophysiology problem makes difficult its numerical solution, requiring temporal and spatial resolutions of 0.1 ms and 0.2 mm respectively for accurate simulations, leading to models with millions degrees of freedom that need to be solved for thousand time steps. Solution of this problem requires the use of algorithms with higher level of parallelism in multi-core platforms. In this regard the newer programmable graphic processing units (GPU) has become a valid alternative due to their tremendous computational horsepower. This paper presents results obtained with a novel electrophysiology simulation software entirely developed in Compute Unified Device Architecture (CUDA). The software implements fully explicit and semi-implicit solvers for the monodomain model, using operator splitting. Performance is compared with classical multi-core MPI based solvers operating on dedicated high-performance computer clusters. Results obtained with the GPU based solver show enormous potential for this technology with accelerations over 50× for three-dimensional problems.

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Fast uncertainty quantification of activation sequences in patient‐specific cardiac electrophysiology meeting clinical time constraints

Quaglino

Pezzuto

Koutsourelakis

et al. 2018

Numer Methods Biomed Eng

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We present a fast, patient-specific methodology for uncertainty quantification in electrophysiology, aimed at meeting the time constraints of clinical practitioners. We focus on computing the statistics of the activation map, given the uncertainties associated with the conductivity tensor modeling the fiber orientation in the heart. We use a fast parallel solution method implemented on a graphics processing unit for the eikonal approximation, in order to compute the activation map and to sample the random fiber field with correlation on the basis of geodesic distances. While this enables to perform uncertainty quantification studies with a manageable computational effort, the required time frame still exceeds clinically suitable time expectations. In order to reduce it further by 2 orders of magnitude, we rely on Bayesian multifidelity methods. In particular, we propose a low-fidelity model that is patient-specific and free from the additional training cost associated with reduced models. This is achieved by a sound physics-based simplification of the full eikonal model. The low-fidelity output is then corrected by the standard multifidelity framework. In practice, the complete procedure only requires approximately 100 new runs of our eikonal graphics processing unit solver for producing the sought estimates and their associated credible intervals, enabling a full online analysis in less than 5 minutes.

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A Brief History of Tissue Models For Cardiac Electrophysiology

Cited by 43 publications

References 102 publications

Determining six cardiac conductivities from realistically large datasets

Determining six cardiac conductivities from realistically large datasets

GPU accelerated solver for nonlinear reaction–diffusion systems. Application to the electrophysiology problem

Fast uncertainty quantification of activation sequences in patient‐specific cardiac electrophysiology meeting clinical time constraints

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