The effects of inhomogeneities and anisotropies on electrocardiographic fields: a 3-D finite-element study

Abstract

The aim of this study was to quantify the effects of selected inhomogeneities and anisotropies on computed electric potential fields associated with the electrocardiographic forward problem. The model construction was based on the Utah Torso model and included geometry for major anatomical structures such as subcutaneous fat, skeletal muscle, and lungs, as well as for epicardial fatpads, major arteries and veins, and the sternum, ribs, spine, and clavicles. Measured epicardial potentials served as the electrical source for solutions to the electrocardiographic forward problems computed using the finite element method (FEM). The geometry of the torso model for each simulation was constant, but different combinations of conductivities were assigned to individual organs or tissues. Comparisons of different conductivity combinations followed one of two basic schemes: 1) a homogeneous torso served as the reference against which we compared simulations with a single organ or tissue and assigned its nominal conductivity, or 2) a fully inhomogeneous torso served as the reference and we removed the effect of individual organs or tissues by assigning it the homogeneous conductivity value. When single inhomogeneities were added to an otherwise homogeneous isotropic model, anisotropic skeletal muscle (at a 15:1 anisotropy ratio) and the right and left lung had larger average effects (12.8, 12.7, and 12.1% relative error (RE), respectively) than the other inhomogeneities tested. Our results for removing single inhomogeneities show that the subcutaneous fat, the anisotropic skeletal muscle (with the degree of anisotropy equal to 7:1), and the lungs have larger average impacts on the body surface potential distributions than other elements of the model (with values of 14.9, 12.6, and 11.7% RE, respectively). The results also show that the size of the effect depended strongly on the distribution of epicardial potentials. The results of this study suggest that accurate representation of tissue inhomogeneity has a significant effect on the accuracy of the forward solution, with regions near the torso surface playing a larger role, in general, than those near the heart.

Influence of white matter inhomogeneous anisotropy on EEG forward computing

“…The unknown ϕ is the electric potential created in the head by the distribution of current from the dipole sources. Then the forward problem is solved by applying the Newman boundary condition as 18,19 (3) where n is the unit surface normal and Γ is the surface.…”

Three-dimensional Multiscale Modelling and Simulation of Atria and Torso Electrophysiology

“…This assignment is used to assign the corresponding organ conductivity to each region as reported in previous studies (MacLeod et al 1991;Klepfer et al 1997;Bradley et al 2000;Bressler & Ding 2006;E. Poletti D. Fiorin & Ruggeri 2009).…”

Study of inhomogeneous head model based on conductivity issues

“…Q, = Q,, on s2 (3) Finally, the problem of potential distribution in head is formed by (l), (2) and (3) [4].…”

“…One of the most common used numerical technique is Finite Element Method (FEM) which has an advantage over other methods for this type of computation since it allows explicit modelling of the inhomogeneity of head tissues. FEM computes an estimate of the potential field over each element, taking into account the material properties of each individual element [2]. Therefor, it is possible to specify different conductivity over different regions, or even for each element.…”

“…The surface potentials which are recorded as electroencephalogram reflect, therefor, both the brain cell generators and the properties of the surrounding volume conductor [ 11. To investigate the effects of the volume conductor on the potential distribution, a model which includes the inhomogeneity of all important head compositions must be constructed [2], [3]. The complexity of most physiological systems far outstrips current modelling ability, further more, the measured conductivity data is very limited and the accuracy varies widely among measured methods and the samples used, as such, models used to study EEG must be limited to a subset of head tissues [l], [2], [3].…”