In this work an optimization-based method of modeling the cardiac activity is presented. The method employs a personalized anatomical 3D model of the patients thorax provided by the segmentation of MRI data as well as an electrophysiological model of the heart.Cellular automaton is used to model the propagation of depolarization and repolarization fronts through the myocardium. The form of action potential (AP) curves was previously derived from the coupled myocardium cell models developed by Noble, Priebe-Beuckelmann and ten Tusscher. The results provided by these three cell models are compared.A series of body surface potential maps (BSPMs) is calculated, the signals on the nodes representing the electrodes are recorded, providing thus a simulated multichannel ECG. A root-mean-square of the difference between simulated and measured ECGs is taken as a criterion for optimization of heart model parameters.The method provides a time-dependent distribution of transmembrane voltages within the heart muscle of a patient.
O. Dössel, W. Bauer, D. Farina, C. Kaltwasser, and O. Skipa. Imaging of bioelectric sources in the heart using a cellular automaton model. In Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, vol. 2, pp. 1067-1070, 2005
The approach to solve the inverse problem of electrocardiography presented here is using a computer model of the individual heart of a patient. It is based on a 3D-MRI dataset. Electrophysiologically important tissue classes are incorporated using rules. Source distributions inside the heart are simulated using a cellular automaton. Finite Element Method is used to calculate the corresponding body surface potential map. Characteristic parameters like duration and amplitude of transmembrane potential or velocity of propagation are optimized for selected tissue classes or regions in the heart so that simulated data fit to the measured data. This way the source distribution and its time course of an individual patient can be reconstructed.
After myocardial infarction, ischemic lesions within the myocardium can be the origin of malignant arrhythmias by the mechanism of re-entry. Surface-ECG and MR-imaging data can be used to detect and classify such re- gions in a non-invasive way. For this purpose a model of the electric conductivity of the tissues within the pa- tients chest and a model of cardiac sources must be constructed out of MR-imaging data. Employing finite- element algorithms the inverse problem of electrocardiology can then be solved, leading to the reconstructionof electrical sources within the myocardium during the process of depolarisation and repolarisation.