6 research outputs found
Quantifying the effect of uncertainty in input parameters in a simplified bidomain model of partial thickness ischaemia
Reduced blood flow in the coronary arteries can lead to damaged heart tissue (myocardial ischaemia). Although one method for detecting myocardial ischaemia involves changes in the ST segment of the electrocardiogram, the relationship between these changes and subendocardial ischaemia is not fully understood. In this study, we modelled ST-segment epicardial potentials in a slab model of cardiac ventricular tissue, with a central ischaemic region, using the bidomain model, which considers conduction longitudinal, transverse and normal to the cardiac fibres. We systematically quantified the effect of uncertainty on the input parameters, fibre rotation angle, ischaemic depth, blood conductivity and six bidomain conductivities, on outputs that characterise the epicardial potential distribution. We found that three typical types of epicardial potential distributions (one minimum over the central ischaemic region, a tripole of minima, and two minima flanking a central maximum) could all occur for a wide range of ischaemic depths. In addition, the positions of the minima were affected by both the fibre rotation angle and the ischaemic depth, but not by changes in the conductivity values. We also showed that the magnitude of ST depression is affected only by changes in the longitudinal and normal conductivities, but not by the transverse conductivities
Modeling of Induced Electric Fields as a Function of Cardiac Anatomy and Venous Pacing Lead Location
A systematic study of head tissue inhomogeneity and anisotropy on EEG forward problem computing
In this study, we propose a stochastic method to
analyze the effects of inhomogeneous anisotropic tissue
conductivity on electroencephalogram (EEG) in forward
computation. We apply this method to an inhomogeneous
and anisotropic spherical human head model. We apply
stochastic finite element method based on Legendre polynomials,Karhunen–Loeve expansion and stochastic
Galerkin methods. We apply Volume and Wang’s constraints
to restrict the anisotropic conductivities for both the
white matter (WM) and the skull tissue compartments. The
EEGs resulting from deterministic and stochastic FEMs are
compared using statistical measurement techniques. Based
on these comparisons, we find that EEGs generated by
incorporating WM and skull inhomogeneous anisotropic
tissue properties individually result in an average of 56.5
and 57.5% relative errors, respectively. Incorporating these
tissue properties for both layers together generate 43.5%
average relative error. Inhomogeneous scalp tissue causes
27% average relative error and a full inhomogeneous
anisotropic model brings in an average of 45.5% relative
error. The study results demonstrate that the effects of
inhomogeneous anisotropic tissue conductivity are significant on EEG