Abstract:

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.

Abstract:

Clinical measurements of atrial excitation propagation were performed to validate the results of noninvasive cardiac source reconstructions. Epicardial potentials and transmembrane voltages were reconstructed using an individual volume conductor model of the patient. Activation times were measured with a basket catheter and compared with the time course of the reconstructed epicardial potentials and transmembrane voltages.

Abstract:

A new approach to the reconstruction of transmembrane potentials (TMP) in anisotropic finite element heart model is presented. The solution is sought in the form of 3D patches constructed by the interpolation of TMP distributions. The method is evaluated using TMP distributions generated with a cellular automaton.

Abstract:

The effect of the modelling errors on the solution of the inverseproblem of electrocardiographyis investigated. The electrocardiographicsignal is simulated using ajnite element model of human torso and realistic source patterns gained with a cellular automaton. Noise is added to simulated measurementsand the inverseproblem is solved. Modelling errors consist offalse conductivity assumptions, changed anisotropy ratio of skeletal muscles and geometric errors. The effect of modeling errors on optimal regulariza- tion parameter determination is investigated. The changes in muscle anisotropy and heart position are shown to have the highest effect on reconstructed epicardial potentials. CRESO and L-curve criteria for optimal regularization parameter estimation are compared.

Abstract:

In this paper, a new method for QRS complex analysis and estimation based on principal component analysis (PCA) and polynomial fitting techniques is presented. Multi-channel ECG signals were recorded and QRS complexes were obtained from every channel and aligned perfectly in matrices. For every channel, the covariance matrix was calculated from the QRS complex data matrix of many heartbeats. Then the corresponding eigenvectors and eigenvalues were calculated and reconstruction parameter vectors were computed by expansion of every beat in terms of the principal eigenvectors. These parameter vectors show short-term fluctuations that have to be discriminated from abrupt changes or long-term trends that might indicate diseases. For this purpose, first-order poly-fit methods were applied to the elements of the reconstruction parameter vectors. In healthy volunteers, subsequent QRS complexes were estimated by calculating the corresponding reconstruction parameter vectors derived from these functions. The similarity, absolute error and RMS error between the original and predicted QRS complexes were measured. Based on this work, thresholds can be defined for changes in the parameter vectors that indicate diseases.

Abstract:

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.

Abstract:

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.

Abstract:

Non invasive source reconstruction from Electrocardiography (ECG) and Magnetocardiography (MCG) data is a highly discussed research field. In this work we investigate mathematically the source space of the Inverse Problem of Electrocardiography with respect to the information content. Starting from the modeled source space of distributed epicardial potentials we compare several ECG electrode configurations, i.e. a 32-, 64-optimized channel electrode arrangement, that we determined for our individual torso model, the 256-lead ECG configuration which was recorded at the Ragnar Granit Instiute, Technical University of Tampere, Finland and the 128 channel system of the BioMag Laboratory, Helsinki University Central Hospital, Finland.

Abstract:

Noninvasive Imaging of the bioelectric processes on the heart using Electrocardiography (ECG) and Magnetocardiography (MCG) data is a widely discussed research topic of the recent years. The source space of ECG is compared with the source space of MCG and vice versa to investigate the difference of information content of these mapping techniques for source imaging purposes. The approach allows the calculation of the intersection and non-intersection part (the calculation of silent sources) of MCG (ECG) in comparison to ECG (MCG). The investigation was carried out on a Finite Element model which was constructed from a magnetic resonance imaging (MRI) dataset of a volunteer. Anisotropic fibre orientation was applied to myocardium to investigate its effect on the differences of the source spaces.