Abstract:

Objective: To investigate cardiac activation maps estimated using electrocardiographic imaging and to find methods reducing line-of-block (LoB) artifacts, while preserving real LoBs. Methods: Body surface potentials were computed for 137 simulated ventricular excitations. Subsequently, the inverse problem was solved to obtain extracellular potentials (EP) and transmembrane voltages (TMV). From these, activation times (AT) were estimated using four methods and compared to the ground truth. This process was evaluated with two cardiac mesh resolutions. Factors contributing to LoB artifacts were identified by analyzing the impact of spatial and temporal smoothing on the morphology of source signals. Results: AT estimation using a spatiotemporal derivative performed better than using a temporal derivative. Compared to deflection-based AT estimation, correlation-based methods were less prone to LoB artifacts but performed worse in identifying real LoBs. Temporal smoothing could eliminate artifacts for TMVs but not for EPs, which could be linked to their temporal morphology. TMVs led to more accurate ATs on the septum than EPs. Mesh resolution had a negligible effect on inverse reconstructions, but small distances were important for cross-correlation-based estimation of AT delays. Conclusion: LoB artifacts are mainly caused by the inherent spatial smoothing effect of the inverse reconstruction. Among the configurations evaluated, only deflection-based AT estimation in combination with TMVs and strong temporal smoothing can prevent LoB artifacts, while preserving real LoBs. Significance: Regions of slow conduction are of considerable clinical interest and LoB artifacts observed in non-invasive ATs can lead to misinterpretations. We addressed this problem by identifying factors causing such artifacts and methods to reduce them.

Abstract:

Ventricular coordinates are widely used as a versatile tool for various applications that benefit from a description of local position within the heart. However, the practical usefulness of ventricular coordinates is determined by their ability to meet application-specific requirements. For regression-based estimation of biventricular position, for example, a symmetric definition of coordinate directions in both ventricles is important. For the transfer of data between different hearts as another use case, the consistency of coordinate values across different geometries is particularly relevant. To meet these requirements, we compare different approaches to compute coordinates and present Cobiveco, a symmetric, consistent and intuitive biventricular coordinate system that builds upon existing coordinate systems, but overcomes some of their limitations. A novel one-way transfer error is introduced to assess the consistency of the coordinates. Normalized distances along bijective trajectories between two boundaries were found to be superior to solutions of Laplace’s equation for defining coordinate values, as they show better linearity in space. Evaluation of transfer and linearity errors on 36 patient geometries revealed a more than 4-fold improvement compared to a state-of-the-art method. Finally, we show two application examples underlining the relevance for cardiac data processing. Cobiveco MATLAB code is available under a permissive open-source license.

Abstract:

Electrocardiographic imaging (ECGI) strongly relies on a priori assumptions and additional information to overcome ill-posedness. The major challenge of obtaining good reconstructions consists in finding ways to add information that effectively restricts the solution space without violating properties of the sought solution. In this work, we attempt to address this problem by constructing a spatio-temporal basis of body surface potentials (BSP) from simulations of many focal excitations. Measured BSPs are projected onto this basis and reconstructions are expressed as linear combinations of corresponding transmembrane voltage (TMV) basis vectors. The novel method was applied to simulations of 100 atrial ectopic foci with three different conduction velocities. Three signal-to-noise ratios (SNR) and bases of six different temporal lengths were considered. Reconstruction quality was evaluated using the spatial correlation coefficient of TMVs as well as estimated local activation times (LAT). The focus localization error was assessed by computing the geodesic distance between true and reconstructed foci. Compared with an optimally parameterized Tikhonov-Greensite method, the BSP basis reconstruction increased the mean TMV correlation by up to 22, 24, and 32% for an SNR of 40, 20, and 0 dB, respectively. Mean LAT correlation could be improved by up to 5, 7, and 19% for the three SNRs. For 0 dB, the average localization error could be halved from 15.8 to 7.9 mm. For the largest basis length, the localization error was always below 34 mm. In conclusion, the new method improved reconstructions of atrial ectopic activity especially for low SNRs. Localization of ectopic foci turned out to be more robust and more accurate. Preliminary experiments indicate that the basis generalizes to some extent from the training data and may even be applied for reconstruction of non-ectopic activity.

Abstract:

Cardiovascular diseases account for 17 million deaths per year worldwide. Of these, 25% are categorized as sudden cardiac death, which can be related to ventricular tachycardia (VT). This type of arrhythmia can be caused by focal activation sources outside the sinus node. Catheter ablation of these foci is a curative treatment in order to inactivate the abnormal triggering activity. However, the localization procedure is usually time-consuming and requires an invasive procedure in the catheter lab. To facilitate and expedite the treatment, we present two novel localization support techniques based on convolutional neural networks (CNNs) that address these clinical needs. In contrast to existing methods, our approaches were designed to be independent of the patient-specific geometry and directly applicable to surface ECG signals, while also delivering a binary transmural position. Moreover, one of the method's outputs can be interpreted as several ranked solutions. The CNNs were trained on a dataset containing only simulated data and evaluated both on simulated test data and clinical data. On a novel large and open simulated dataset, the median test error was below 3 mm. The median localization error on the unseen clinical data ranged from 32 mm to 41 mm without optimizing the pre-processing and CNN to the clinical data. Interpreting the output of one of the approaches as ranked solutions, the best median error of the top-3 solutions decreased to 20 mm on the clinical data. The transmural position was correctly detected in up to 82% of all clinical cases. These results demonstrate a proof of principle to utilize CNNs to localize the activation source without the intrinsic need for patient-specific geometrical information. Furthermore, providing multiple solutions can assist physicians in identifying the true activation source amongst more than one possible location. With further optimization to clinical data, these methods have high potential to accelerate clinical interventions, replace certain steps within these procedures and consequently reduce procedural risk and improve VT patient outcomes.

Abstract:

PURPOSE: Atrial fibrillation is one of the most frequent cardiac arrhythmias in the industrialized world and ablation therapy is the method of choice for many patients. However, ablation scars alter the electrophysiological activation and the mechanical behavior of the affected atria. Different ablation strategies with the aim to terminate atrial fibrillation and prevent its recurrence exist but their impact on the performance of the heart is often neglected. METHODS: In this work, we present a simulation study analyzing five commonly used ablation scar patterns and their combinations in the left atrium regarding their impact on the pumping function of the heart using an electromechanical whole-heart model. We analyzed how the altered atrial activation and increased stiffness due to the ablation scars affect atrial as well as ventricular contraction and relaxation. RESULTS: We found that systolic and diastolic function of the left atrium is impaired by ablation scars and that the reduction of atrial stroke volume of up to 11.43% depends linearly on the amount of inactivated tissue. Consequently, the end-diastolic volume of the left ventricle, and thus stroke volume, was reduced by up to 1.4 and 1.8%, respectively. During ventricular systole, left atrial pressure was increased by up to 20% due to changes in the atrial activation sequence and the stiffening of scar tissue. CONCLUSION: This study provides biomechanical evidence that atrial ablation has acute effects not only on atrial contraction but also on ventricular performance. Therefore, the position and extent of ablation scars is not only important for the termination of arrhythmias but is also determining long-term pumping efficiency. If confirmed in larger cohorts, these results have the potential to help tailoring ablation strategies towards minimal global cardiovascular impairment.

Abstract:

The arrhythmogenesis of atrial fibrillation is associated with the presence of fibrotic atrial tissue. Not only fibrosis but also physiological anatomical variability of the atria and the thorax reflect in altered morphology of the P wave in the 12-lead electrocardiogram (ECG). Distinguishing between the effects on the P wave induced by local atrial substrate changes and those caused by healthy anatomical variations is important to gauge the potential of the 12-lead ECG as a non-invasive and cost-effective tool for the early detection of fibrotic atrial cardiomyopathy to stratify atrial fibrillation propensity. In this work, we realized 54,000 combinations of different atria and thorax geometries from statistical shape models capturing anatomical variability in the general population. For each atrial model, 10 different volume fractions (0-45%) were defined as fibrotic. Electrophysiological simulations in sinus rhythm were conducted for each model combination and the respective 12-lead ECGs were computed. P wave features (duration, amplitude, dispersion, terminal force in V1) were extracted and compared between the healthy and the diseased model cohorts. All investigated feature values systematically in- or decreased with the left atrial volume fraction covered by fibrotic tissue, however value ranges overlapped between the healthy and the diseased cohort. Using all extracted P wave features as input values, the amount of the fibrotic left atrial volume fraction was estimated by a neural network with an absolute root mean square error of 8.78%. Our simulation results suggest that although all investigated P wave features highly vary for different anatomical properties, the combination of these features can contribute to non-invasively estimate the volume fraction of atrial fibrosis using ECG-based machine learning approaches.

Abstract:

OBJECTIVE: Atrial flutter (AFl) is a common arrhythmia that can be categorized according to different self-sustained electrophysiological mechanisms. The non-invasive discrimination of such mechanisms would greatly benefit ablative methods for AFl therapy as the driving mechanisms would be described prior to the invasive procedure, helping to guide ablation. In the present work, we sought to implement recurrence quantification analysis (RQA) on 12-lead ECG signals from a computational framework to discriminate different electrophysiological mechanisms sustaining AFl. METHODS: 20 different AFl mechanisms were generated in 8 atrial models and were propagated into 8 torso models via forward solution, resulting in 1,256 sets of 12-lead ECG signals. Principal component analysis was applied on the 12-lead ECGs, and six RQA-based features were extracted from the most significant principal component scores in two different approaches: individual component RQA and spatial reduced RQA. RESULTS: In both approaches, RQA-based features were significantly sensitive to the dynamic structures underlying different AFl mechanisms. Hit rate as high as 67.7% was achieved when discriminating the 20 AFl mechanisms. RQA-based features estimated for a clinical sample suggested high agreement with the results found in the computational framework. CONCLUSION: RQA has been shown an effective method to distinguish different AFl electrophysiological mechanisms in a non-invasive computational framework. A clinical 12-lead ECG used as proof of concept showed the value of both the simulations and the methods. SIGNIFICANCE: The non-invasive discrimination of AFl mechanisms helps to delineate the ablation strategy, reducing time and resources required to conduct invasive cardiac mapping and ablation procedures.

Abstract:

BACKGROUND Atrial !brillation (AF) is the most common supra- ventricular arrhythmia, characterized by disorganized atrial electri- cal activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. OBJECTIVES To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electro- cardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. METHODS AFdriverswereinducedin2computerizedatrialmodels and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Bi- nary decision tree classi!er was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subse- quently isolated in the models to assess PVI success Finally, the classi!er was tested on a clinical dataset (46 patients: 23 PV- dependent AF and 23 with additional extra-PV sources). RESULTS The classi!er yielded 82.6% speci!city and 73.9% sensi- tivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. CONCLUSION Machine learning–based classi!cation of 12-lead- ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI. KEYWORDS Atrial !brillation; Atrial ablation; Machine learning; Noninvasive; 12-lead electrocardiogram; Pulmonary vein isolation; Cardiac simulations (Cardiovascular Digital Health Journal 2021;2:126–136) © 2021 Heart Rhythm Society. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc- nd/4.0/).

Abstract:

Background: Hypertrophic cardiomyopathy (HCM) is typically caused by mutations in sarcomeric genes leading to cardiomyocyte disarray, replacement fibrosis, impaired contractility, and elevated filling pressures. These varying tissue properties are associ- ated with certain strain patterns that may allow to establish a diagnosis by means of non-invasive imaging without the necessity of harmful myocardial biopsies or con- trast agent application. With a numerical study, we aim to answer: how the variability in each of these mechanisms contributes to altered mechanics of the left ventricle (LV) and if the deformation obtained in in-silico experiments is comparable to values reported from clinical measurements. Methods: We conducted an in-silico sensitivity study on physiological and pathologi- cal mechanisms potentially underlying the clinical HCM phenotype. The deformation of the four-chamber heart models was simulated using a finite-element mechanical solver with a sliding boundary condition to mimic the tissue surrounding the heart. Furthermore, a closed-loop circulatory model delivered the pressure values acting on the endocardium. Deformation measures and mechanical behavior of the heart mod- els were evaluated globally and regionally. Results: Hypertrophy of the LV affected the course of strain, strain rate, and wall thickening—the root-mean-squared difference of the wall thickening between control (mean thickness 10 mm) and hypertrophic geometries (17 mm) was >10%. A reduc- tion of active force development by 40% led to less overall deformation: maximal radial strain reduced from 26 to 21%. A fivefold increase in tissue stiffness caused a more homogeneous distribution of the strain values among 17 heart segments. Fiber disarray led to minor changes in the circumferential and radial strain. A combination of pathological mechanisms led to reduced and slower deformation of the LV and halved the longitudinal shortening of the LA. Conclusions: This study uses a computer model to determine the changes in LV deformation caused by pathological mechanisms that are presumed to underlay HCM. This knowledge can complement imaging-derived information to obtain a more accu- rate diagnosis of HCM.

Abstract:

In both clinical and computational studies, different pacing protocols are used to induce arrhythmia and non-inducibility is often considered as the endpoint of treatment. The need for a standardized methodology is urgent since the choice of the protocol used to induce arrhythmia could lead to contrasting results, e.g., in assessing atrial fibrillation (AF) vulnerabilty. Therefore, we propose a novel method-pacing at the end of the effective refractory period (PEERP)-and compare it to state-of-the-art protocols, such as phase singularity distribution (PSD) and rapid pacing (RP) in a computational study. All methods were tested by pacing from evenly distributed endocardial points at 1 cm inter-point distance in two bi-atrial geometries. Seven different atrial models were implemented: five cases without specific AF-induced remodeling but with decreasing global conduction velocity and two persistent AF cases with an increasing amount of fibrosis resembling different substrate remodeling stages. Compared with PSD and RP, PEERP induced a larger variety of arrhythmia complexity requiring, on average, only 2.7 extra-stimuli and 3 s of simulation time to initiate reentry. Moreover, PEERP and PSD were the protocols which unveiled a larger number of areas vulnerable to sustain stable long living reentries compared to RP. Finally, PEERP can foster standardization and reproducibility, since, in contrast to the other protocols, it is a parameter-free method. Furthermore, we discuss its clinical applicability. We conclude that the choice of the inducing protocol has an influence on both initiation and maintenance of AF and we propose and provide PEERP as a reproducible method to assess arrhythmia vulnerability.

Abstract:

Mathematical models of the human heart are evolving to become a cornerstone of precision medicine and support clinical decision making by providing a powerful tool to understand the mechanisms underlying pathophysiological conditions. In this study, we present a detailed mathematical description of a fully coupled multi-scale model of the human heart, including electrophysiology, mechanics, and a closed-loop model of circulation. State-of-the-art models based on human physiology are used to describe membrane kinetics, excitation-contraction coupling and active tension generation in the atria and the ventricles. Furthermore, we highlight ways to adapt this framework to patient specific measurements to build digital twins. The validity of the model is demonstrated through simulations on a personalized whole heart geometry based on magnetic resonance imaging data of a healthy volunteer. Additionally, the fully coupled model was employed to evaluate the effects of a typical atrial ablation scar on the cardiovascular system. With this work, we provide an adaptable multi-scale model that allows a comprehensive personalization from ion channels to the organ level enabling digital twin modeling

Abstract:

The ECG is one of the most commonly used non-invasive tools to gain insights into the electrical functioning of the heart. It has been crucial as a foundation in the creation and validation of in silico models describing the underlying electrophysiological processes. However, so far, the contraction of the heart and its influences on the ECG have mainly been overlooked in in silico models. As the heart contracts and moves, so do the electrical sources within the heart responsible for the signal on the body surface, thus potentially altering the ECG. To illuminate these aspects, we developed a human 4-chamber electro-mechanically coupled whole heart in silico model and embedded it within a torso model. Our model faithfully reproduces measured 12-lead ECG traces, circulatory characteristics, as well as physiological ventricular rotation and atrioventricular valve plane displacement. We compare our dynamic model to three non-deforming ones in terms of standard clinically used ECG leads (Einthoven and Wilson) and body surface potential maps (BSPM). The non-deforming models consider the heart at its ventricular end-diastatic, end-diastolic and end-systolic states. The standard leads show negligible differences during P-Wave and QRS-Complex, yet during T-Wave the leads closest to the heart show prominent differences in amplitude. When looking at the BSPM, there are no notable differences during the P-Wave, but effects of cardiac motion can be observed already during the QRS-Complex, increasing further during the T-Wave. We conclude that for the modeling of activation (P-Wave/QRS-Complex), the associated effort of simulating a complete electro-mechanical approach is not worth the computational cost. But when looking at ventricular repolarization (T-Wave) in standard leads as well as BSPM, there are areas where the signal can be influenced by cardiac motion of the heart to an extent that should not be ignored.

Abstract:

Large-scale electrophysiological simulations to obtain electrocardiograms (ECG) carry the potential to pro- duce extensive datasets for training of machine learning classifiers to, e.g., discriminate between different cardiac pathologies. The adoption of simulations for these purposes is limited due to a lack of ready-to- use models covering atrial anatomical variability. We built a bi-atrial statistical shape model (SSM) of the endocardial wall based on 47 segmented human CT and MRI datasets using Gaussian process morphable models. Generalization, specificity, and compact- ness metrics were evaluated. The SSM was applied to simulate atrial ECGs in 100 random volumetric instances. The first eigenmode of our SSM reflects a change of the total volume of both atria, the second the asym- metry between left vs. right atrial volume, the third a change in the prominence of the atrial appendages. The SSM is capable of generalizing well to unseen geometries and 95% of the total shape variance is cov- ered by its first 24 eigenvectors. The P waves in the 12-lead ECG of 100 random instances showed a duration of 109 . 7 ±12 . 2 ms in accordance with large cohort studies. The novel bi-atrial SSM itself as well as 100 exemplary instances with rule-based augmentation of atrial wall thickness, fiber orientation, inter-atrial bridges and tags for anatomical structures have been made publicly available. This novel, openly available bi-atrial SSM can in future be employed to generate large sets of realistic atrial geometries as a basis for in silico big data approaches.

Abstract:

The solution of the inverse problem of electrocardiology allows the reconstruction of the spatial distribution of the electrical activity of the heart from the body surface electrocardiogram (electrocardiographic imaging, ECGI). ECGI using the equivalent dipole layer (EDL) model has shown to be accurate for cardiac activation times. However, validation of this method to determine repolarization times is lacking. In the present study, we determined the accuracy of the EDL model in reconstructing cardiac repolarization times, and assessed the robustness of the method under less ideal conditions (addition of noise and errors in tissue conductivity). A monodomain model was used to determine the transmembrane potentials in three different excitation-repolarization patterns (sinus beat and ventricular ectopic beats) as the gold standard. These were used to calculate the body surface ECGs using a finite element model. The resulting body surface electrograms (ECGs) were used as input for the EDL-based inverse reconstruction of repolarization times. The reconstructed repolarization times correlated well (COR > 0.85) with the gold standard, with almost no decrease in correlation after adding errors in tissue conductivity of the model or noise to the body surface ECG. Therefore, ECGI using the EDL model allows adequate reconstruction of cardiac repolarization times. Graphical abstract Validation of electrocardiographic imaging for repolarization using forward calculated body surface ECGs from simulated activation-repolarization sequences.

Abstract:

Radiofrequency ablation (RFA) therapy is the gold standard in interventional treatment of many cardiac arrhythmias. A major obstacle are non transmural lesions, leading to recurrence of arrhythmias. Recent clinical studies have suggested intracardiac electrogram (EGM) criteria as a promising marker to evaluate lesion development. Seeking for a deeper understanding of underlying mechanisms, we established a simulation approach for acute RFA lesions. Ablation lesions were modeled by a passive necrotic core surrounded by a borderzone with properties of heated myocardium. Herein, conduction velocity and electrophysiological properties were altered. We simulated EGMs during RFA to study the relation between lesion formation and EGM changes using the bidomain model. Simulations were performed on a three dimensional setup including a geometrically detailed representation of the catheter with highly conductive electrodes. For validation, EGMs recorded during RFA procedures in five patients were analyzed and compared to simulation results. Clinical data showed major changes in the distal unipolar EGM. During RFA, the negative peak amplitude decreased up to 104% and maximum negative deflection was up to 88% smaller at the end of the ablation sequence. These changes mainly occurred in the first 10 s after ablation onset. Simulated unipolar EGM reproduced the clinical changes, reaching up to 83% negative peak amplitude reduction and 80% decrease in maximum negative deflection for transmural lesions. In future work, the established model may enable the development of further EGM criteria for transmural lesions even for complex geometries in order to support clinical therapy.

Abstract:

We suggest a new regularization method for reconstruction of cardiac transmembrane voltages (TMV) from body surface potentials that is based on imposing similarity between time-aligned TMVs. An iterative scheme is proposed to update the delays needed for time-alignment. Evaluation of the method using simulated ventricular pacings showed a clear improvement over second order Tikhonov.

Abstract:

ECG imaging estimates the cardiac electrical activity from body surface potentials. As this involves solving a severly ill-posed problem, additional information is required to get a unique and stable solution. Recent progress is based on introducing more problem-specific information by exploiting the structure of cardiac excitation. However, added information must be either certain or general enough to not impair the solution. We have recently developed a method that uses a spatio-temporal basis to restrict the solution space. In the present work, we analyzed this method with respect to one of the most fundamental assumptions made during basis creation: cardiac (an)isotropy. We tested the reconstruction using simulations of ventricular pacings and then applied it to clinical data. In simulations, the overall median localization error was smallest with a basis including fiber orientation. For the clinical data, however, the overall error was smallest with an isotropic basis. This observation suggests that modeling priors should be introduced with care, whereby further work is needed.

Abstract:

The boundary element method is widely used to solve the forward problem of electrocardiography, i.e. to calculate the body surface potentials (BSP) caused by the heart’s electrical activity. This requires discretization of boundary surfaces between compartments of a torso model. Often, the resolution of the surface bounding the heart is chosen above 1 mm, which can lead to spikes in resulting BSPs. We demonstrate that this artifact is caused by discontinuous propagation of the wavefront on coarse meshes and can be avoided by blurring cardiac sources before spatial downsampling. We evaluate different blurring methods and show that Laplacian blurring reduces the BSP error 5-fold for both transmembrane voltages and extracellular potentials downsampled to 3 different resolutions. We suggest a method to find the optimal blurring parameter without having to compute BSPs using a fine mesh.

Abstract:

ECG imaging aims to reconstruct the cardiac electrical activity from non-invasive measurements of body surface potentials (BSP) by finding unique and physiologically meaningful solutions to the inverse problem of electrocardiography. This can be accomplished using regularization, which reduces the space of admissible solutions by demanding solution properties that are already known beforehand. Messnarz et. al. proposed a regularization scheme that requires transmembrane voltages (TMV) to not decrease over time. We suggest a generalization of this method that forces TMVs to decrease only slowly and as a result can also be applied to irregular cardiac activity. We first develop the method using a simplified spherical geometry and then show its benefit for imaging fibrillatory activity on a realistic geometry of the atria.

Abstract:

Modelling the interaction of the heart and the vascular system allows to study the pumping efficiency of the heart in a controlled environment under various cardiac and vascular conditions such as arrhythmias, dyssynchronies, regions of stiffened myocardium, valvular stenoses or decreased vascular compliances. To pose realistic hemodynamic boundary conditions to a four-chambered elastomechanical heart model, we developed a lumped element model of the closed loop human vascular system. Systemic and pulmonary circulations were each represented by a three-element Windkessel model emptying into a venous compliance. Both circulations were coupled by connecting the venous compliances to the corresponding atrium via venous resistances. Cardiac valves were represented by ideal diodes and resistances. Strong coupling between the heart and the vascular system model was accomplished by estimating the cardiac pressures that lead to continuous flows across the model interfaces. Active regulatory mechanisms were not considered. Pressures, flows and volumes throughout the circulatory system were simulated until a steady state was reached and the effects of model parameters on these hemodynamic parameters were evaluated in a sensitivity analysis. Increasing the systemic peripheral resistance by 50% caused an 8% decrease in stroke volume (SV) and a 33% increase in mean arterial pressure. Increased venous resistance descreased the E/A wave ratio of the atrioventricular flow and led to a reduced SV by impeding passive cardiac filling. Increasing the arterial compliance decreased mean cardiac pressures, while only slightly reducing the SV. Larger arterial resistances mainly caused higher peak systolic pressures. Furthermore, we show that embedding the heart model into surrounding elastic tissue by forcing permanent contact at the pericardial surface leads to more realistic time courses of atrial volumes and atrial pressure-volume curves composed of an A and a V loop as found in measurements. In conclusion, this work enables simulations of diseases that involve significant cardiovascular interaction.

Abstract:

Electrocardiographic imaging (ECGI) could help in diagnosis and treatment of atrial fibrillation (AF), the most common life-threatening arrhythmia. Based on a previous work by Figuera et al. on the reconstruction of epicardial potentials (EP) during AF, we explore the performance of a Tikhonov regularization with two spatial constraints for transmembrane voltage (TMV) based ECGI. We develop a new method to impose epi-endocardial similarity and show its benefit for ECGI of atrial activity. Apart from TMVs, local activation times and dominant frequency are evaluated as target parameters. In the AF models studied, joint reconstruction of epi- and endocardial TMVs showed performance comparable to the previously reported EPs imaging.

Abstract:

Intracardiac electrograms are essential for the diagnosis and treatment of various cardiac arrhythmias. To gain reliable information about structural alterations of underlying tissue, it is necessary to interpret these electrograms correctly. Therefore it has to be understood how other parameters influence the signal. Realistic 3D geometries were created and simulated using the bidomain model. Based on these simulations, the influences of catheter orientation, tissue thickness and conduction velocity on the amplitudes of intracardiac electrograms were evaluated.

Abstract:

Lumped parameter models of the human circulatory sys-tem are able to reproduce major features and phases of human circulation. However, they often lack physiological detail regarding pressure and flow across the valves. To alleviate these shortcomings, we implement a model of heart valve dynamics based on Bernoulli's principle to account for the transvalvular pressure drop and extend it by smooth opening and closing of the valves. We evaluate the new model based on a simulation with healthy valves and explore the possibility of simulating heart valve diseases by considering a case of severe aortic stenosis. The model more faithfully reproduces pressure, volume, and flow in all four chambers and in particular across the valves. Most of the changes are related to the consideration of blood inertia. However, only by opening and closing the valves more slowly, it is possible to reproduce features connected to backflow. When reducing the maximum area ratio of the aortic valve to 10%, a pressure gradient of 77.2 mmHg during systole and a 20% reduction in stroke volume was observed in accordance with the AHA guidelines of severe aortic stenosis. To conclude, we were able to improve our existing OD circulation model in terms of physiological accuracy by replacing the diode-like valves with an easy to implement model of heart valve dynamics that is capable of simulating both healthy and pathological scenarios.

Abstract:

Individualized computer models of the geometry of the human heart are often based on mag- netic resonance images (MRI) or computed tomography (CT) scans. The stress distribution in the imaged state cannot be measured but needs to be estimated from the segmented geometry, e.g. by an iterative algorithm. As the convergence of this algorithm depends on different geometrical conditions, we system- atically studied their influence. Beside various shape alterations, we investigated the chamber volume, as well as the effect of material parameters. We found a marked influence of passive material parameters: increasing the model stiffness by a factor of ten halved the residual norm in the first iteration. Flat and concave areas led to a reduced robustness and convergence rate of the unloading algorithm. With this study, the geometric effects and modeling aspects governing the unloading algorithm’s convergence are identified and can be used as a basis for further improvement.

Abstract:

Atrial flutter (AFl) is a common heart rhythm disorder driven by different self-sustaining electrophysiological atrial mechanisms. In the present work, we sought to discriminate which mechanism is sustaining the arrhythmia in an individual patient using non-invasive 12-lead electrocardiogram (ECG) signals. Specifically, we analyse the influence of atrial and torso geometries for the success of such discrimination. 2,512 ECG were simulated and 151 features were extracted from the signals. Three classification scenarios were investigated: random set classification; leave-one-atrium-out (LOAO); and leave-one-torso-out (LOTO). A radial basis neural network classifier achieved test accuracies of 89.84%, 88.98%, and 59.82% for the random set classification, LOTO, and LOAO, respectively. The most discriminative single feature was the F-wave duration (74% test accuracy). Our results show that a machine learning approach can potentially identify a high number of different AFl mechanisms using the 12-lead ECG. More than the 8 atrial models used in this work should be included during training due to the significant influence that the atrial geometry has on the ECG signals and thus on the resulting classification. This non-invasive classification can help to identify the optimal ablation strategy, reducing time and resources required to conduct invasive cardiac mapping and ablation procedures.

Abstract:

A variety of biophysical and phenomenological active tension models has been proposed during the last decade that show physiological behaviour on a cellular level. However, applying these models in a whole heart finite element simulation framework yields either unphysiological values of stress and strain or an insufficient deformation pattern compared to magnetic resonance imaging data. In this study, we evaluate how introducing an orthotropic active stress tensor affects the deformation pattern by conducting a sensitivity analysis regarding the active tension at resting length Tref and three orthotropic activation parameters (Kss, Ksn and Knn). Deformation of left ventricular contraction is evaluated on a truncated ellipsoid using four features: wall thickening (WT), longitudinal shortening (LS), torsion (Θ) and ejection fraction (EF). We show that EF, WT and LS are positively correlated with the parameters Tref and Knn while Kss reduces all of the four observed features. Introducing shear stress to the model has little to no effect on EF, WT and LS, although it reduces torsion by up to 3◦. We find that added stress in the normal direction can support healthy deformation patterns. However, the twisting motion, which has been shown to be important for cardiac function, reduces by up to 20◦.

Abstract:

Over the last decades, computational models have been applied in in-silico simulations of the heart biomechan- ics. These models depend on input parameters. In particular, four parameters are needed for the constitutive law of Guc- cione et al., a model describing the stress-strain relation of the heart tissue. In the literature, we could find a wide range of values for these parameters. In this work, we propose an optimization framework which identifies the parameters of a constitutive law. This framework is based on experimental measurements conducted by Klotz et al.. They provide an end-diastolic pressure-volume relation- ship. We applied the proposed framework on one heart model and identified the following elastic parameters to optimally match the Klotz curve: 𝐶 = 313 Pa, 𝑏𝑓 = 17.8, 𝑏𝑡 = 7.1 and 𝑏𝑓𝑡 = 12.4. In general, this approach allows to identify optimized param- eters for a constitutive law, for a patient-specific heart geome- try. The use of optimized parameters will lead to physiological simulation results of the heart biomechanics and is therefore an important step towards applying computational models in clinical practice.

Abstract:

Activation times (AT) describe the sequence of cardiac depolarization and represent one of the most important parameters for analysis of cardiac electrical activity. However, estimation of ATs can be challenging due to multiple sources of noise such as fractionation or baseline wander. If ATs are estimated from signals reconstructed using electrocardiographic imaging (ECGI), additional problems can arise from over-smoothing or due to ambiguities in the inverse problem. Often, resulting AT maps show falsely homogeneous regions or artificial lines of block. As ATs are not only important clinically, but are also commonly used for evaluation of ECGI methods, it is important to understand where these errors come from. We present results from a community effort to compare methods for AT estimation on a common dataset of simulated ventricular pacings. ECGI reconstructions were performed using three different surface source models: transmembrane voltages, epi-endo potentials and pericardial potentials, all using 2nd-order Tikhonov and 6 different regularization parameters. ATs were then estimated by the community participants and compared to the ground truth. While the pacing site had the largest effect on AT correlation coefficients (CC larger for lateral than for septal pacings), there were also differences between methods and source models that were poorly reflected in CCs. Results indicate that artificial lines of block are most severe for purely temporal methods. Compared to the other source models, ATs estimated from transmembrane voltages are more precise and less prone to artifacts.

Abstract:

Electrocardiographic Imaging (ECGI) requires robust ECG forward simulations to accurately calculate cardiac activity. However, many questions remain regarding ECG forward simulations, for instance: there are not common guidelines for the required cardiac source sampling. In this study we test equivalent double layer (EDL) forward simulations with differing cardiac source resolutions and different spatial interpolation techniques. The goal is to reduce error caused by undersampling of cardiac sources and provide guidelines to reduce said source undersampling in ECG forward simulations. Using a simulated dataset sampled at 5 spatial resolutions, we computed body surface potentials using an EDL forward simulation pipeline. We tested two spatial interpolation methods to reduce error due to undersampling triangle weighting and triangle splitting. This forward modeling pipeline showed high frequency artifacts in the predicted ECG time signals when the cardiac source resolution was too low. These low resolutions could also cause shifts in extrema location on the body surface maps. However, these errors in predicted potentials can be mitigated by using a spatial interpolation method. Using spatial interpolation can reduce the number of nodes required for accurate body surface potentials from 9,218 to 2,306. Spatial interpolation in this forward model could also help improve accuracy and reduce computational cost in subsequent ECGI applications.

Abstract:

Nowadays, a large share of the global population is affected by heart rhythm disorders. Computational modelling is a useful tool for understanding the dynamics of cardiac arrhythmias. Several recent clinical and experimental studies suggest that atrial fibrillation is maintained by re-entrant drivers (e.g. rotors). As a consequence, numerous works have addressed atrial arrhythmogenicity of a given electrophysiological model using different methods to simulate the perpetuation of re-entrant activity. However, no common procedure to test atrial fibrillation vulnerability has yet been defined. Here, we systematically evaluate and compare two state-of-the-art methods. The first one is rapid extrastimulus pacing from rim of the four pulmonary veins. The second consists of placing phase singularities in the atria, estimating an activation time map by solving the Eikonal equation and finally using this as initial condition for the electrical cardiac propagation simulation. In this way, we are forcing the wavefronts to follow re-entrant circuits with low computational cost thus less simulation time. We aim to identify a methodology to quantify arrhythmia vulnerability on patient-specific atrial geometries and substrates. We will proceed with in-silico experiments, comparing the results of these two methods to initiate re-entrant activity, checking the influence of the different parameters on the dynamics on the re-entrant drivers and finally extracting a valid set of parameters allowing to reliably assess re-entry vulnerability. The final objective is to come up with an easily reproducible minimal set of simulations to assess vulnerability of a particular atrial substrate (cellular and tissue model) or of distinct anatomical atrial geometries to arrhythmic episodes. Given the great need of exploring susceptibility to atrial arrhythmias, i.e. after a first ablation procedure, this study can provide a useful tool to test new treatment strategies and to learn how to prevent the onset and progression of atrial fibrillation.

Abstract:

The human heart is an organ of high complexity and hence, very challenging to simulate. To calculate the force developed by the human heart and therefore the tension of the muscle fibers, accurate models are necessary. The force generated by the cardiac muscle has physiologically imposed limits and depends on various characteristics such as the length, strain and the contraction velocity of the cardiomyocytes. Another characteristic is the activation time of each cardiomyocyte, which is a wave and not a static value for all cardiomyocytes. To simulate a physiologically correct excitation, the functionality of the cardiac simulation framework CardioMechanics was extended to incorporate inhomogeneous activation times. The functionality was then used to evaluate the effects of local activation times with two different tension models. The active stress generated by the cardiomyocytes was calculated by (i) an explicit function and (ii) an ode-based model. The results of the simulations showed that the maximum pressure in the left ventricle dropped by 2.3% for the DoubleHill model and by 5.3% for the Lumens model. In the right ventricle the simulations showed similar results. The maximum pressure in both the left and the right atrium increased using both models. Given that the simulation of the inhomogeneously activated cardiomyocytes increases the simulation time when used with the more precise Lumens model, the small drop in maximum pressure seems to be negligible in favor of a simpler simulation model.

Abstract:

Radiofrequency ablation (RFA) is a widely used clinical treatment for many types of cardiac arrhythmias. However, nontransmural lesions and gaps between linear lesions often lead to recurrence of the arrhythmia. Intrac- ardiac electrograms (IEGMs) provide real-time informa- tion regarding the state of the cardiac tissue surrounding the catheter tip. Nevertheless, the formation and inter- pretation of IEGMs during the RFA procedure is complex and yet not fully understood. In this in-silico study, we propose a computational model for acute ablation lesions. Our model consists of a necrotic scar core and a border zone, describing irreversible and reversible temperature induced electrophysiological phenomena. These phenom- ena are modeled by varying the intra- and extracellular conductivity of the tissue as well as a regulating zone factor. The computational model is evaluated regarding its feasibility and validity. Therefore, this model was com- pared to an existing one and to clinical measurements of ve patients undergoing RFA. The results show that the model can indeed be used to recreate IEGMs. We computed IEGMs arising from complex ablation scars, such as scars with gaps or two overlapping ellipsoid scars. For orthogo- nal catheter orientation, the presence of a second necrotic core in the near- eld of a punctiform acute ablation lesion had minor impact on the resulting signal morphology. The presented model can serve as a base for further research on the formation and interpretation of IEGMs.

Abstract:

Creating transmural ablation scars in a reliable way is a key issue in improvement of therapeutical pro- cedures for cardiac arrhythmias. About one third of the patients has to undergo several procedures till arrhythmic episodes are successfully treated. Morphological features of intracardiac electrograms might contribute to evaluate scar transmurality during the ablation procedure. We an- alyzed intracardiac signals before, during and after point- wise ablation in patients with atrial flutter. Unipolar elec- trograms of the distal electrode showed a relative decrease in amplitude of the second extremum of up to 99 % with a mean of 84±20.6 % after the endpoint of ablation.

Abstract:

Intracardiac electrograms are the key in under- standing, interpretation and treatment of cardiac arrhythmias. However, electrogram morphologies are strongly variable due to catheter position, orientation and contact. Simulations of intracardiac electrograms can improve comprehension and quantification of influencing parameters and therefore reduce misinterpretations. In this study simulated intracardiac electro- grams are analyzed regarding tilt angles of the catheter relative to the propagation direction, electrode tissue distances as well as clinical filter settings. Catheter signals are computed on a realistic 3D catheter geometry using bidomain simulations of cardiac electrophysiology. Thereby high conductivities of the catheter electrodes are taken into account. For validation, simulated electrograms are compared with in vivo electrograms recorded during an EP-study with direct annotation of catheter orientation and tissue contact. Good agreement was reached regarding timing and signal width of simulated and measured electrograms. Correlation was 0.92±0.07 for bipolar, 0.92±0.05 for unipolar distal and 0.80 ± 0.12 for unipolar proximal electrograms for different catheter orientations and locations.

Abstract:

Local activation time (LAT) maps help to understand the path of electrical excitation in cardiac arrhythmias. They can be generated automatically from intracardiac electrograms using various criteria provided by commercial electroanatomical mapping systems. This study compares existing criteria and a novel method based on the non-linear energy operator (NLEO) with respect to their precision and robustness.

Abstract:

17 million deaths a year worldwide are linked to cardiovascular diseases. Sudden cardiac death is one result in approximately 25% of all patients with cardiovascular diseases and may be connected to ventricular tachycardia. When treating the ventricular tachycardia with a catheter intervention, the detection of the so-called exit points, i.e. the spatial origin of the excitation, is a crucial step. As this procedure is very time consuming and skilled cardiologists are required, there is a need for assisting localization procedures, preferably automatic and non-invasive ones. Electrocardiographic imaging tries to meet these needs by reconstructing the electrical activity of the heart from body surface potential measurements. The resulting information can be used to reconstruct the excitation origin. However, current methods for solving this inverse problem show either low precision or poor robustness, which limits their clinical utility. This work first analyzes the forward problem in combination with two source models: transmembrane voltages and extracellular potentials. The mathematical properties of the relation between sources on the heart and the body surface potentials are analyzed systematically and the impact on the inverse problem is explained and visualized. Subsequently, this knowledge is used to solve the inverse problem. Three novel methods are introduced: Delay-based regularization, body surface potential regression, and deep learning-based localization. These three methods are compared to four state-of-the-art methods using one simulated and two clinical datasets. On the simulated as well as one clinical dataset, one of the novel methods outperformed the existing approaches, whereas on the remaining clinical dataset, Tikhonov regularization performed best. Potential reasons for these results are discussed and related to properties of the forward problem.