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M Tinová

Publications and source records attributed to M Tinová.

2 recordsLinked to original sources

The uniform double layer model and myocardial infarction: forward solution consideration.

The Uniform Double Layer (UDL) model of the cardiac generator is often used for forward simulation of body surface potentials (BSPs). The model also proved to be very useful for the inverse computation of heart activation. However, for the purposes of Myocardial Infarction (MI) modelling mostly the Multiple Dipole (MD) models are used. In our study, the ability of UDL model to represent the activation of the heart with an old MI was examined. The finite element model of the heart was used to simulate electrical activation of the heart with an old MI. Different locations of endocardial MI were used. For each of them three cases were considered according to the scale of the infarcted area: small and medium endocardial and large transmural. For the further computation of the electric field within the torso volume conductor two types of UDL representation of the cardiac generator were used. For the first UDL model, supposing the scared tissue to be unexcitable, an "infarcted" surface (different from the "healthy" surface) of activated myocardium was generated for each case of MI. Times when activation wavefront reached particular nodes on the surface served as an input for the forward computation of BSPs. To be able to understand the behaviour of the UDL, we also created the second UDL model, where the "infarcted activation sequence" was approximated on the original "healthy" heart surface. The BSPs were computed for each case of MI using both UDL cardiac generators. The boundary element method with the inhomogeneous volume conductor was used for computations. The BSPs generated by both models for the same case of MI were compared using the correlation coefficient. The results show, that it is possible to find an approximation of the "infarcted activation sequence" on the "healthy" heart generator surface in a way that BSPs generated by both models have a correlation coefficient higher than 0.96 for the entire period of depolarisation. Visualisation of the epicardial isochrones might help to understand the UDL model behaviour under the MI conditions. It would be useful for the correct interpretation of the results when using the UDL model for inverse solution. (Fig. 7, Ref. 5.)

Body Surface Potential Mapping↗

Model study of influence of extracardial factors on the inverse localization of preexcitation sites.

Inverse solution techniques are expected to help in noninvasive localization of ventricular preexcitation sites. The influence of selected extracardial factors on the accuracy of the inverse localization of the initial activation sites was studied on a model. Each of 8 simulated activation sequences was initiated in a different single starting point at the atrioventricular ring. Corresponding ecg potentials on the surface of a realistic model of inhomogeneous torso were used for the inverse localization procedure. A multiple dipole (MD) model of the cardiac generator composed of 39 segmental dipoles was used in the inverse computations. As it was shown in a previous study, the method was able to localize the 8 starting points even if a simplified torso model and a limited number of leads was used. In this study, influence of another two factors was evaluated: inaccuracy of location of the MD generator and presence of noise in surface potentials. Several shifts and rotations of the heart generator relative to its exact position were modeled. When the mean deviation of starting points was about 1 cm the mean localization error varied from 0.5 cm up to 1.0 cm for complete model data--198 surface potentials and a torso model including lungs and ventricular cavities. When a noise with uniform and Gaussian distribution was added to the surface potentials, the use of averaged body surface potentials significantly improved accuracy and stability of the inverse solution. For root mean square value of noise sigma = 14 microV the mean error of localization was 0.9 cm. For higher noise (sigma = 30 microV) the results were substantially deteriorated. The influence of a noise was studied on complete model data. (Tab. 3, Fig. 5. Ref. 6.)

Body Surface Potential Mapping↗