David U. J. Keller

Ranking the Influence of Tissue Conductivities on Forward-Calculated ECGs

This paper examined the effects that different tissue conductivities had on forward-calculated ECGs. To this end, we ranked the influence of tissues by performing repetitive forward calculations while varying the respective tissue conductivity. The torso model included all major anatomical structures like blood, lungs, fat, anisotropic skeletal muscle, intestine, liver, kidneys, bone, cartilage, and spleen. Cardiac electrical sources were derived from realistic atrial and ventricular simulations. The conductivity rankings were based on one of two methods: First, we considered fixed percental conductivity changes to probe the sensitivity of the ECG regarding conductivity alterations. Second, we set conductivities to the reported minimum and maximum values to evaluate the effects of the existing conductivity uncertainties. The amplitudes of both atrial and ventricular ECGs were most sensitive for blood, skeletal muscle conductivity and anisotropy as well as for heart, fat, and lungs. If signal morphology was considered, fat was more important whereas skeletal muscle was less important. When comparing atria and ventricles, the lungs had a larger effect on the atria yet the heart conductivity had a stronger impact on the ventricles. The effects of conductivity uncertainties were significant. Future studies dealing with electrocardiographic simulations should consider these effects.

Modeling atrial fiber orientation in patient-specific geometries: a semi-automatic rule-based approach

Atrial myofiber orientation is complex and has multiple discrete layers and bundles. A novel robust semi-automatic method to incorporate atrial anisotropy and heterogeneities into patient-specific models is introduced. The user needs to provide 22 distinct seed-points from which a network of auxiliary lines is constructed. These are used to define fiber orientation and myocardial bundles. The method was applied to 14 patient-specific volumetric models derived from CT, MRI and photographic data. Initial electrophysiological simulations show a significant influence of anisotropy and heterogeneity on the excitation pattern and P-wave duration (20.7% shortening). Fiber modeling results show good overall correspondence with anatomical data. Minor modeling errors are observed if more than four pulmonary veins exist in the model. The method is an important step towards creating realistic patient-specific atrial models for clinical applications.

Personalization of Atrial Anatomy and Electrophysiology as a Basis for Clinical Modeling of Radio-Frequency Ablation of Atrial Fibrillation

Multiscale cardiac modeling has made great advances over the last decade. Highly detailed atrial models were created and used for the investigation of initiation and perpetuation of atrial fibrillation. The next challenge is the use of personalized atrial models in clinical practice. In this study, a framework of simple and robust tools is presented, which enables the generation and validation of patient-specific anatomical and electrophysiological atrial models. Introduction of rule-based atrial fiber orientation produced a realistic excitation sequence and a better correlation to the measured electrocardiograms. Personalization of the global conduction velocity lead to a precise match of the measured P-wave duration. The use of a virtual cohort of nine patient and volunteer models averaged out possible model-specific errors. Intra-atrial excitation conduction was personalized manually from left atrial local activation time maps. Inclusion of LE-MRI data into the simulations revealed possible gaps in ablation lesions. A fast marching level set approach to compute atrial depolarization was extended to incorporate anisotropy and conduction velocity heterogeneities and reproduced the monodomain solution. The presented chain of tools is an important step towards the use of atrial models for the patient-specific AF diagnosis and ablation therapy planing.

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Despite the commonly accepted notion that action potential duration (APD) is distributed heterogeneously throughout the ventricles and that the associated dispersion of repolarization is mainly responsible for the shape of the T-wave, its concordance and exact morphology are still not completely understood. This paper evaluated the T-waves for different previously measured heterogeneous ion channel distributions. To this end, cardiac activation and repolarization was simulated on a high resolution and anisotropic biventricular model of a volunteer. From the same volunteer, multichannel ECG data were obtained. Resulting transmembrane voltage distributions for the previously measured heterogeneous ion channel expressions were used to calculate the ECG and the simulated T-wave was compared to the measured ECG for quantitative evaluation. Both exclusively transmural (TM) and exclusively apico-basal (AB) setups produced concordant T-waves, whereas interventricular (IV) heterogeneities led to notched T-wave morphologies. The best match with the measured T-wave was achieved for a purely AB setup with shorter apical APD and a mix of AB and TM heterogeneity with M-cells in midmyocardial position and shorter apical APD. Finally, we probed two configurations in which the APD was negatively correlated with the activation time. In one case, this meant that the repolarization directly followed the sequence of activation. Still, the associated T-waves were concordant albeit of low amplitude.

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The Purkinje network plays a major role for realistically simulating the activation sequence of the ventricles. In this work, we describe a method to create an endocardial stimulation profile that describes the location and time instant of ventricular stimulation, thus mimicking the His-Purkinje conduction system. By adapting model parameters stimulation profiles can be generated for different ventricular anatomies with minimal manual interaction. The stimulation profile parameters are evaluated by analyzing the excitation propagation in a three-dimensional, heterogeneous and anisotropic model of the human ventricles which are embedded in an anatomically detailed torso geometry. The calculated QRS complexes are in good agreement with the corresponding clinical recordings on the same proband.

Heterogeneities on the Genesis of the T-wave: A Computational Evaluation

Heterogeneity of ion channel properties within human ventricular tissue determines the sequence of repolarization under healthy conditions. In this computational study, the impact of different extend of electrophysiological heterogeneity in both human ventricles on the ECG was investigated by a forward calculation of the cardiac electrical signals on the body surface. The gradients ranged from solely transmural, interventricular and apico-basal up to full combination of these variations. As long interventricular heterogeneities were neglected, the transmural gradient generated a positive T wave that was increased when apico-basal variations were considered. Inclusion of interventricular changes necessitated the incorporation of both transmural and apico-basal heterogeneities to reproduce the positive T wave.

Fast Creation of Endocardial Stimulation Profiles for the Realistic Simulation of Body Surface ECGs

In this paper, we present an efficient method to estimate changes in forward-calculated body surface potential maps (BSPMs) caused by variations in tissue conductivities. For blood, skeletal muscle, lungs, and fat, the influence of conductivity variations was analyzed using the principal component analysis (PCA). For each single tissue, we obtained the first PCA eigenvector from seven sample simulations with conductivities between ±75% of the default value. We showed that this eigenvector was sufficient to estimate the signal over the whole conductivity range of ±75%. By aligning the origins of the different PCA coordinate systems and superimposing the single tissue effects, it was possible to estimate the BSPM for combined conductivity variations in all four tissues. Furthermore, the method can be used to easily calculate confidence intervals for the signal, i.e., the minimal and maximal possible amplitudes for given conductivity uncertainties. In addition to that, it was possible to determine the most probable conductivity values for a given BSPM signal. This was achieved by probing hundreds of different conductivity combinations with a numerical optimization scheme. In conclusion, our method allows to efficiently predict forward-calculated BSPMs over a wide range of conductivity values from few sample simulations.

Modeling of heterogeneous electrophysiology in the human heart with respect to ECG genesis

High performance computing is required to make feasible simulations of whole organ models of the heart with biophysically detailed cellular models in a clinical setting. Increasing model detail by simulating electrophysiology and mechanical models increases computation demands. We present scaling results of an electro — mechanical cardiac model of two ventricles and compare them to our previously published results using an electrophysiological model only. The anatomical data-set was given by both ventricles of the Visible Female data-set in a 0.2 mm resolution. Fiber orientation was included. Data decomposition for the distribution onto the distributed memory system was carried out by orthogonal recursive bisection. Load weight ratios for non — tissue vs. tissue elements used in the data decomposition were 1:1, 1:2, 1:5, 1:10, 1:25, 1:38.85, 1:50 and 1:100. The ten Tusscher et al. (2004) electrophysiological cell model was used and the Rice et al. (1999) model for the computation of the calcium transient dependent force. Scaling results for 512, 1024, 2048, 4096, 8192 and 16,384 processors were obtained for 1 ms simulation time. The simulations were carried out on an IBM Blue Gene/L supercomputer. The results show linear scaling from 512 to 16,384 processors with speedup factors between 1.82 and 2.14 between partitions. The most optimal load ratio was 1:25 for on all partitions. However, a shift towards load ratios with higher weight for the tissue elements can be recognized as can be expected when adding computational complexity to the model while keeping the same communication setup. This work demonstrates that it is potentially possible to run simulations of 0.5 s using the presented electro-mechanical cardiac model within 1.5 hours.

Predicting Tissue Conductivity Influences on Body Surface Potentials—An Efficient Approach Based on Principal Component Analysis

Many studies conducted on patients suffering from congestive heart failure have shown the efficacy of cardiac resynchronization therapy (CRT). The presented research investigates an off-line optimization algorithm based on different electrode positioning and timing delays. A computer model of the heart was used to simulate left bundle branch block (LBBB), myocardial infarction (MI) and reduction of intraventricular conduction velocity in order to customize the patient symptom. The optimization method evaluates the error between the healthy heart and pathology with/without pacing in terms of activation time and QRS length. Additionally, a torso model of the patient is extracted to compute the body surface potential map (BSPM) and to simulate the ECG with Wilson leads to validate the results obtained by the electrophysiological heart model optimization.

Strong scaling and speedup to 16,384 processors in cardiac electro — Mechanical simulations

Simulation of cardiac excitation is often a trade-off between accuracy and speed. A promising minimal, time-efficient cell model with four state variables has recently been presented together with parametrizations for ventricular cell behaviour. In this work, we adapt the model parameters to reproduce atrial excitation properties as given by the Courtemanche model. The action potential shape is considered as well as the restitution of action potential duration and conduction velocity. Simulation times in a single cell and a tissue patch are compared between the two models. We further present the simulation of a sinus beat on the atria in a realistic 3D geometry using the fitted minimal model in a monodomain simulation.