Abstract:

The bidomain model and the finite element method are an established standard to mathematically describe cardiac electrophysiology, but are both suboptimal choices for fast and large-scale simulations due to high computational costs. We investigate to what extent simplified approaches for propagation models (monodomain, reaction-Eikonal and Eikonal) and forward calculation (boundary element and infinite volume conductor) deliver markedly accelerated, yet physiologically accurate simulation results in atrial electrophysiology. <i>Methods:</i> We compared action potential durations, local activation times (LATs), and electrocardiograms (ECGs) for sinus rhythm simulations on healthy and fibrotically infiltrated atrial models. <i>Results:</i> All simplified model solutions yielded LATs and P&#x00A0;waves in accurate accordance with the bidomain results. Only for the Eikonal model with pre-computed action potential templates shifted in time to derive transmembrane voltages, repolarization behavior notably deviated from the bidomain results. ECGs calculated with the boundary element method were characterized by correlation coefficients <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula>0.9 compared to the finite element method. The infinite volume conductor method led to lower correlation coefficients caused predominantly by systematic overestimations of P&#x00A0;wave amplitudes in the precordial leads. <i>Conclusion:</i> Our results demonstrate that the Eikonal model yields accurate LATs and combined with the boundary element method precise ECGs compared to markedly more expensive full bidomain simulations. However, for an accurate representation of atrial repolarization dynamics, diffusion terms must be accounted for in simplified models. <i>Significance:</i> Simulations of atrial LATs and ECGs can be notably accelerated to clinically feasible time frames at high accuracy by resorting to the Eikonal and boundary element methods.

Abstract:

Left atrial enlargement (LAE) is one of the risk factors for atrial fibrillation (AF). A non-invasive and automated detection of LAE with the 12-lead electrocardiogram (ECG) could therefore contribute to an improved AF risk stratification and an early detection of new-onset AF incidents. However, one major challenge when applying machine learning techniques to identify and classify cardiac diseases usually lies in the lack of large, reliably labeled and balanced clinical datasets. We therefore examined if the extension of clinical training data by simulated ECGs derived from a novel bi-atrial shape model could improve the automated detection of LAE based on P waves of the 12-lead ECG. We derived 95 volumetric geometries from the bi-atrial statistical shape model with continuously increasing left atrial volumes in the range of 30 ml to 65 ml. Electrophysiological simulations with 10 different conduction velocity settings and 2 different torso models were conducted. Extracting the P waves of the 12-lead ECG thus yielded a synthetic dataset of 1,900 signals. Besides the simulated data, 7,168 healthy and 309 LAE ECGs from a public clinical ECG database were available for training and testing of an LSTM network to identify LAE. The class imbalance of the training data could be reduced from 1:23 to 1:6 when adding simulated data to the training set. The accuracy evaluated on the test dataset comprising a subset of the clinical ECG recordings improved from 0.91 to 0.95 if simulated ECGs were included as an additional input for the training of the classifier. Our results suggest that using a bi-atrial statistical shape model as a basis for ECG simulations can help to overcome the drawbacks of clinical ECG recordings and can thus lead to an improved performance of machine learning classifiers to detect LAE based on the 12-lead ECG.

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:

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 morphology of the electrocardiogram (ECG) varies among different healthy subjects due to anatomical and structural reasons, such as for example the shape of the heart geometry or the position and size of surrounding organs in the torso. Knowledge about these ECG morphology changes could be used to parameterize electrophysiological simula- tions of the human heart. In this work, we detected the boundaries of ECG waveforms, i.e. the P-wave, the QRS-complex and the T-wave, in 12- lead ECGs from 918 healthy subjects in the Physionet Com- puting in Cardiology Challenge 2020 Database with the IBT openECG toolbox. Subsequently, we obtained the onset, the peak and the offset of each P-wave, QRS-complex and T-wave in the signal. In this way, the duration of the P-wave, the QRS- complex and the T-wave, the PQ-, RR- and the QT-interval as well as the amplitudes of the P-wave, the Q-, R- and S- peak and the T-wave in each lead were extracted from the 918 healthy ECGs. Their statistical distributions and correlation between each other were assessed. The highest variabilities among the 918 healthy subject were found for the RR interval and the amplitudes of the QRS- complex. The highest correlation was observed for feature pairs that represent the same feature in different leads. Es- pecially the R-peak amplitudes showed a strong correlation across different leads. The calculated feature distributions can be used to optimize the parameters of populations of cardiac electrophysiological models. In this way, realistic in-silico generated surface ECGs can be simulated in large scale and could be used as input data for machine learning algorithms for a classification of cardio- vascular diseases.

Abstract:

Background and Aims Patients with persistent atrial fibrillation (AF) experience 50% recurrence despite pulmonary vein isolation (PVI), and no consensus is established for second treatments. The aim of our i-STRATIFICATION study is to provide evidence for stratifying patients with AF recurrence after PVI to optimal pharmacological and ablation therapies, through in-silico trials.Methods A cohort of 800 virtual patients, with variability in atrial anatomy, electrophysiology, and tissue structure (low voltage areas, LVA), was developed and validated against clinical data from ionic currents to ECG. Virtual patients presenting AF post-PVI underwent 13 secondary treatments.Results Sustained AF developed in 522 virtual patients after PVI. Second ablation procedures involving left atrial ablation alone showed 55% efficacy, only succeeding in small right atria (<60mL). When additional cavo-tricuspid isthmus ablation was considered, Marshall-Plan sufficed (66% efficacy) for small left atria (<90mL). For bigger left atria, a more aggressive ablation approach was required, such as anterior mitral line (75% efficacy) or posterior wall isolation plus mitral isthmus ablation (77% efficacy). Virtual patients with LVA greatly benefited from LVA ablation in the left and right atria (100% efficacy). Conversely, in the absence of LVA, synergistic ablation and pharmacotherapy could terminate AF. In the absence of ablation, the patient’s ionic current substrate modulated the response to antiarrhythmic drugs, being the inward currents critical for optimal stratification to amiodarone or vernakalant.Conclusion In-silico trials identify optimal strategies for AF treatment based on virtual patient characteristics, evidencing the power of human modelling and simulation as a clinical assisting tool.

Abstract:

Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often disagree and they are inherently difficult to evaluate due to a lack of ground truth beyond synthetic datasets. In this work, we put feature importance methods to the test on real-world data in the domain of cardiology, where we try to distinguish three specific pathologies from healthy subjects based on ECG features comparing to features used in cardiologists' decision rules as ground truth. We found that the SHAP and LIME methods and Chi-squared test all worked well together with the native Random forest and Logistic regression feature rankings. Some methods gave inconsistent results, which included the Maximum Relevance Minimum Redundancy and Neighbourhood Component Analysis methods. The permutation-based methods generally performed quite poorly. A surprising result was found in the case of left bundle branch block, where T-wave morphology features were consistently identified as being important for diagnosis, but are not used by clinicians.

Abstract:

Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.

Abstract:

Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.

Abstract:

AIMS: Electro-anatomical voltage, conduction velocity (CV) mapping, and late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) have been correlated with atrial cardiomyopathy (ACM). However, the comparability between these modalities remains unclear. This study aims to (i) compare pathological substrate extent and location between current modalities, (ii) establish spatial histograms in a cohort, (iii) develop a new estimated optimized image intensity threshold (EOIIT) for LGE-MRI identifying patients with ACM, (iv) predict rhythm outcome after pulmonary vein isolation (PVI) for persistent atrial fibrillation (AF). METHODS AND RESULTS: Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Late gadolinium enhancement areas were classified using the UTAH, image intensity ratio (IIR >1.20), and new EOIIT method for comparison to low-voltage substrate (LVS) and slow conduction areas <0.2 m/s. Receiver operating characteristic analysis was used to determine LGE thresholds optimally matching LVS. Atrial cardiomyopathy was defined as LVS extent ≥5% of the left atrium (LA) surface at <0.5 mV. The degree and distribution of detected pathological substrate (percentage of individual LA surface are) varied significantly (P < 0.001) across the mapping modalities: 10% (interquartile range 0-14%) of the LA displayed LVS <0.5 mV vs. 7% (0-12%) slow conduction areas <0.2 m/s vs. 15% (8-23%) LGE with the UTAH method vs. 13% (2-23%) using IIR >1.20, with most discrepancies on the posterior LA. Optimized image intensity thresholds and each patient's mean blood pool intensity correlated linearly (R2 = 0.89, P < 0.001). Concordance between LGE-MRI-based and LVS-based ACM diagnosis improved with the novel EOIIT applied at the anterior LA [83% sensitivity, 79% specificity, area under the curve (AUC): 0.89] in comparison to the UTAH method (67% sensitivity, 75% specificity, AUC: 0.81) and IIR >1.20 (75% sensitivity, 62% specificity, AUC: 0.67). CONCLUSION: Discordances in detected pathological substrate exist between LVS, CV, and LGE-MRI in the LA, irrespective of the LGE detection method. The new EOIIT method improves concordance of LGE-MRI-based ACM diagnosis with LVS in ablation-naive AF patients but discrepancy remains particularly on the posterior wall. All methods may enable the prediction of rhythm outcomes after PVI in patients with persistent AF.

Abstract:

INTRODUCTION: Improved sinus rhythm (SR) maintenance rates have been achieved in patients with persistent atrial fibrillation (AF) undergoing pulmonary vein isolation plus additional ablation of low voltage substrate (LVS) during SR. However, voltage mapping during SR may be hindered in persistent and long-persistent AF patients by immediate AF recurrence after electrical cardioversion. We assess correlations between LVS extent and location during SR and AF, aiming to identify regional voltage thresholds for rhythm-independent delineation/detection of LVS areas. (1) Identification of voltage dissimilarities between mapping in SR and AF. (2) Identification of regional voltage thresholds that improve cross-rhythm substrate detection. (3) Comparison of LVS between SR and native versus induced AF. METHODS: Forty-one ablation-naive persistent AF patients underwent high-definition (1 mm electrodes; >1200 left atrial (LA) mapping sites per rhythm) voltage mapping in SR and AF. Global and regional voltage thresholds in AF were identified which best match LVS < 0.5 mV and <1.0 mV in SR. Additionally, the correlation between SR-LVS with induced versus native AF-LVS was assessed. RESULTS: Substantial voltage differences (median: 0.52, interquartile range: 0.33-0.69, maximum: 1.19 mV) with a predominance of the posterior/inferior LA wall exist between the rhythms. An AF threshold of 0.34 mV for the entire left atrium provides an accuracy, sensitivity and specificity of 69%, 67%, and 69% to identify SR-LVS < 0.5 mV, respectively. Lower thresholds for the posterior wall (0.27 mV) and inferior wall (0.3 mV) result in higher spatial concordance to SR-LVS (4% and 7% increase). Concordance with SR-LVS was higher for induced AF compared to native AF (area under the curve[AUC]: 0.80 vs. 0.73). AF-LVS < 0.5 mV corresponds to SR-LVS < 0.97 mV (AUC: 0.73). CONCLUSION: Although the proposed region-specific voltage thresholds during AF improve the consistency of LVS identification as determined during SR, the concordance in LVS between SR and AF remains moderate, with larger LVS detection during AF. Voltage-based substrate ablation should preferentially be performed during SR to limit the amount of ablated atrial myocardium.

Abstract:

Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists' decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data.

Abstract:

AIMS: The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy. METHODS AND RESULTS: Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5-6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models. CONCLUSION: The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins.

Abstract:

The KCNQ1 gene encodes the α-subunit of the cardiac voltage-gated potassium (Kv) channel KCNQ1, also denoted as Kv7.1 or KvLQT1. The channel assembles with the ß-subunit KCNE1, also known as minK, to generate the slowly activating cardiac delayed rectifier current IKs, a key regulator of the heart rate dependent adaptation of the cardiac action potential duration (APD). Loss-of-function variants in KCNQ1 cause the congenital Long QT1 (LQT1) syndrome, characterized by delayed cardiac repolarization and a QT interval prolongation in the surface electrocardiogram (ECG). Autosomal dominant loss-of-function variants in KCNQ1 result in the LQT syndrome called Romano-Ward syndrome (RWS), while autosomal recessive variants affecting function, lead to Jervell and Lange-Nielsen syndrome (JLNS), associated with deafness. The aim of this study was the characterization of novel KCNQ1 variants identified in patients with RWS to widen the spectrum of known LQT1 variants, and improve the interpretation of the clinical relevance of variants in the KCNQ1 gene. We functionally characterized nine human KCNQ1 variants using the voltage-clamp technique in Xenopus laevis oocytes, from which we report seven novel variants. The functional data was taken as input to model surface ECGs, to subsequently compare the functional changes with the clinically observed QTc times, allowing a further interpretation of the severity of the different LQTS variants. We found that the electrophysiological properties of the variants correlate with the severity of the clinically diagnosed phenotype in most cases, however, not in all. Electrophysiological studies combined with in silico modelling approaches are valuable components for the interpretation of the pathogenicity of KCNQ1 variants, but assessing the clinical severity demands the consideration of other factors that are included, for example in the Schwartz score.

Abstract:

Interatrial conduction block refers to a disturbance in the propagation of electrical impulses in the conduction pathways between the right and the left atrium. It is a risk factor for atrial fibrillation, stroke, and premature death. Clin- ical diagnostic criteria comprise an increased P wave dura- tion and biphasic P waves in lead II, III and aVF due to ret- rograde activation of the left atrium. Machine learning algo- rithms could improve the diagnosis but require a large-scale, well-controlled and balanced dataset. In silico electrocardio- gram (ECG) signals, optimally obtained from a statistical shape model to cover anatomical variability, carry the poten- tial to produce an extensive database meeting the requirements for successful machine learning application. We generated the first in silico dataset including interatrial conduction block of 9,800simulated ECG signals based on a bi-atrial statistical shape model. Automated feature analysis was performed to evaluate P wave morphology, duration and P wave terminal force in lead V1. Increased P wave duration and P wave ter- minal force in lead V1 were found for models with interatrial conduction block compared to healthy models. A wide vari- ability of P wave morphology was detected for models with in- teratrial conduction block. Contrary to previous assumptions, our results suggest that a biphasic P wave morphology seems to be neither necessary nor sufficient for the diagnosis of in- teratrial conduction block. The presented dataset is ready for a classification with machine learning algorithms and can be easily extended.

Abstract:

Aims Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.

Abstract:

The electrocardiogram (ECG) is a standard cost-efficient and non-invasive tool for the early detection of various cardiac diseases. Quantifying different timing and amplitude features of and in between the single ECG waveforms can reveal important information about the underlying (dys-)function of the heart. Determining these features requires the detection of fiducial points that mark the on- and offset as well as the peak of each ECG waveform (P wave, QRS complex, T wave). Manually setting these points is time-consuming and requires a physician’s expert knowledge. Therefore, the highly modular ECGdeli toolbox for MATLAB was developed, which is capable of filtering clinically recorded 12-lead ECG signals and detecting the fiducial points, also called delineation. It is one of the few open toolboxes offering ECG delineation for P waves, T Waves and QRS complexes. The algorithms provided were evaluated with the QT database, an ECG database comprising 105 signals with fiducial points annotated by clinicians. The median difference between the fiducial points set by the boundary detection algorithm and the clinical annotations serving as a ground truth is less than 4 samples (16 ms) for the P wave and the QRS complex markers.

Abstract:

Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.

Abstract:

Quantifying the atrial conduction velocity (CV) reveals important information for targeting critical arrhythmia sites that initiate and sustain abnormal electrical pathways, e.g. during atrial flutter. The knowledge about the local CV distribution on the atrial surface thus enhances clinical catheter ablation procedures by localizing pathological propagation paths to be eliminated during the intervention. Several algorithms have been proposed for estimating the CV. All of them are solely based on the local activation times calculated from electroanatomical mapping data. They deliver false values for the CV if applied to regions near scars or wave collisions. We propose an extension to all approaches by including a distinct preprocessing step. Thereby, we first identify scars and wave front collisions and provide this information for the CV estimation algorithm. In addition, we provide reliable CV values even in the presence of noise. We compared the performance of the Triangulation, the Polynomial Fit and the Radial Basis Functions approach with and without the inclusion of the aforementioned preprocessing step. The evaluation was based on different activation patterns simulated on a 2D synthetic triangular mesh with different levels of noise added. The results of this study demonstrate that the accuracy of the estimated CV does improve when knowledge about the depolarization pattern is included. Over all investigated test cases, the reduction of the mean velocity error quantified to at least 25 mm/s for the Radial Basis Functions, 14 mm/s for the Polynomial Fit and 14 mm/s for the Triangulation approach compared to their respective implementations without the preprocessing step. Given the present results, this novel approach can contribute to a more accurate and reliable CV estimation in a clinical setting and thus improve the success of radio-frequency ablation to treat cardiac arrhythmias.

Abstract:

Persistent atrial fibrillation (AF) patients show a 50% recurrence after pulmonary vein isolation (PVI), and no consensus is established for following treatment. The aim of our i-STRATIFICATION study is to provide evidence for optimal stratification of recurrent AF patients to pharmacological versus ablation therapy, through insilico trials in 800 virtual atria. The cohort presents variability in anatomy, electrophysiology, and tissue structure (low voltage areas, LVA), and is developed and validated against experimental and clinical data from ionic currents to ECG. AF maintenance is evaluated prior-and post-PVI, and atria with sustained arrhythmia after PVI are independently subjected to seven state-of-the-art treatments for AF. The results of the i-STRATICICATION study show that the right and left atrial volume dictate the success of ablation therapy in structurally-healthy atria. On the other hand, LVA ablation, both in the right and left atrium, is required for atria presenting LVA remodelling and short refractoriness. This atrial refractoriness, mainly modulated by L-type Ca2+ current, ICaL, and fast Na+ current, INa, determines the success of pharmacological therapy. Therefore, our study suggests the assessment of optimal treatment selection using the above-mentioned patient characteristics. This provides digital evidence to integrate human in-silico trials into clinical practice.

Abstract:

Clinical and computational studies highlighted the role of atrial anatomy for atrial fibrillation vulnerability. However, personalized computational models are often generated from electroanatomical maps, which might lack important anatomical structures like the appendages, or from imaging data which are potentially affected by segmentation uncertainty. A bi-atrial statistical shape model (SSM) covering relevant structures for electrophysiological simulations was shown to cover atrial shape variability. We hypothesized that it could, therefore, also be used to infer the shape of missing structures and deliver ready-to-use models to assess atrial fibrillation vulnerability in silico. We implemented a highly automatized pipeline to generate a personalized computational model by fitting the SSM to the clinically acquired geometries. We applied our framework to a geometry coming from an electroanatomical map and one derived from magnetic resonance images (MRI). Only landmarks belonging to the left atrium and no information from the right atrium were used in the fitting process. The left atrium surface-to-surface distance between electroanatomical map and a fitted instance of the SSM was 2.26+-1.95 mm. The distance between MRI segmentation and SSM was 2.07+-1.56 mm and 3.59+-2.84 mm in the left and right atrium, respectively. Our semi-automatic pipeline provides ready-to-use personalized computational models representing the original anatomy well by fitting a SSM. We were able to infer the shape of the right atrium even in the case of using information only from the left atrium.

Abstract:

An early detection and diagnosis of atrial fibrillation sets the course for timely intervention to prevent potentially occurring comorbidities. Electrocardiogram data resulting from electrophysiological cohort modeling and simulation can be a valuable data resource for improving automated atrial fibrillation risk stratification with machine learning techniques and thus, reduces the risk of stroke in affected patients.