Estimating conductivity distribution of transmural wedges of the ventricle using parallel genetic algorithms

In this work we use a computational human left ventricular wedge to simulate regions of abnormal intra- and extra-cellular conductivities that mimic some known pathological conditions. We propose a method based on genetic algorithms that aims on estimating the distribution of intra- and extra-cellular conductivities, by comparing cardiac simulations to some given transmural electrograms. The methods were developed for distributed systems and the results were obtained in a cluster composed of 8 computers interconnected by a fast network switching device. The results suggest that the proposed method is able to approximately estimate both intra- and extra-cellular conductivity distributions from transmural electrograms with an accuracy of 40%.