This clinical consensus document proposes standardized atrial segments for 3D imaging, electroanatomical mapping and computational modelling, based on anatomical, electrophysiological and clinical considerations, with precise definitions of regional borders allowing for reproducible and automated regionalization. 3D imaging and high-resolution electroanatomical mapping have become an integral part of cardiac electrophysiology and the management of patients with arrhythmias. However, to perform regional quantitative analyses and intra- and inter-individual, as well as cross-modality comparisons, a universal definition of atrial regions and their boundaries is required. While for the left ventricle there is already an established standardized regionalization (AHA 17-segment model), there is no such consensus for the atria. In a multi-disciplinary writing group consisting of cardiologists, cardiac electrophysiologists, cardiovascular imaging specialists, and anatomists as well as specialists in computational cardiac modelling from European Heart Rhythm Association and European Association of Cardiovascular Imaging, a standardized regionalization based on a 15-segment bi-atrial model was elaborated. This clinical consensus document will enable consistent regional analyses and homogeneous data acquisition across different centres and modalities, and may thus have a significant impact on atrial arrhythmia research and personalized treatment approaches based on individual arrhythmia patterns and phenotypes.

This review focuses on the computerized modeling of the electrophysiology of the human atria, emphasizing the simulation of common arrhythmias such as atrial flutter (AFlut) and atrial fibrillation (AFib). Which components of the model are necessary to accurately model arrhythmogenic tissue modifications, including remodeling, cardiomyopathy, and fibrosis, to ensure reliable simulations? The central question explored is the level of detail required for trustworthy simulations for a specific context of use. The review discusses the balance between model complexity and computational efficiency, highlighting the risks of oversimplification and excessive detail. It covers various aspects of atrial modeling, from cellular to whole atria levels, including the influence of atrial geometry, fiber direction, anisotropy, and wall thickness on simulation outcomes. The article also examines the impact of different modeling approaches, such as volumetric 3D models, bilayer models, and single surface models, on the realism of simulations. In addition, it reviews the latest advances in the modeling of fibrotic tissue and the verification and validation of atrial models. The intended use of these models in planning and optimization of atrial ablation strategies is discussed, with a focus on personalized modeling for individual patients and cohort-based approaches for broader applications. The review concludes by emphasizing the importance of integrating experimental data and clinical validation to enhance the utility of computerized atrial models to improve patient outcomes.

The international Working Group of the Signal Summit is a consortium of experts in the field of cardiac electrophysiology dedicated to advancing knowledge on understanding and clinical application of signal recording and processing techniques. In 2023, the working group met in Reykjavik, Iceland, and laid the foundation for this manuscript. Atrial fibrillation (AF) is the most common arrhythmia in adults, with a rapidly increasing prevalence worldwide. Despite substantial research efforts, advancements in elucidating the underlying mechanisms of AF have been relatively modest. Since the discovery of pulmonary veins as a frequent trigger region for AF initiation more than 2½ decades ago, advancements in patient care have primarily focused on technologic innovations to improve the safety and efficacy of pulmonary vein isolation (PVI). Several factors may explain the limited scientific progress made. First, whereas AF initiation usually begins with an ectopic beat, the mechanisms of initiation, maintenance, and electrical propagation have not been fully elucidated in humans, largely owing to suboptimal spatiotemporal mapping. Second, underlying structural changes have not been clarified and may involve different types of reentry. Third, inconsistent definitions and terminology regarding fibrillatory characteristics contribute to the challenges of comparing results between studies. Fourth, a growing appreciation for phenotypical differences probably explains the wide range of clinical outcomes to catheter ablation in patients with seemingly similar AF types. Last, restoring sinus rhythm in advanced phenotypic forms of AF is often not feasible or may require extensive ablation with minimal or no positive impact on quality of life. The aims of this international position paper are to provide practical definitions as a foundation for discussing potential mechanisms and mapping results and to propose pathways toward meaningful advancements in AF research, ultimately leading to improved therapies for AF.

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.

Introduction The role of the right atrium (RA) in atrial fibrillation (AF) has long been overlooked. Computer models of the atria can aid in assessing how the RA influences arrhythmia vulnerability and in studying the role of RA drivers in the induction of AF, both aspects challenging to assess in living patients. It remains unclear if incorporating the RA influences the reentry inducibility of the model. As personalized ablation strategies rely on non-inducibility criteria, the adequacy of left atrium (LA)-only models for developing such ablation tools is uncertain. Aim To evaluate the effect of incorporating the RA in 3D patient-specific computer models on arrhythmia vulnerability. Methods Imaging data from 8 subjects were obtained to generate patient-specific computer models. We created 2 models for each subject: a monoatrial with only the LA and a biatrial with both the RA and LA. We considered 3 different states of substrate remodeling: healthy (H), mild (M), and severe (S). The Courte-manche et al. cellular model was modified from control conditions to a setup representing AF-induced remodeling with 0 %, 50 %, and 100 % changes for H, M, and S, respectively. Conduction velocity was set to 1.2, 1.0, and 0.8 m/s for each remodeling state. Fibrosis extent corresponded to Utah 2 (5-20 %) and Utah 4 ( > 35 %) stages for M and S, while the H state was modeled without fibrosis. Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different points separated by 2cm using openCARP. A point was classified as inducing arrhythmia if reentry was maintained for at least 1 s. The vulnerability ratio was defined as the number of inducing points divided by the number of stimulation points. The mean tachycardia cycle length (TCL) was assessed at the stimulation site. We compared LA vulnerability ratios in monoatrial and biatrial models. Results Incorporating the RA increased the mean LA vulnerability ratio by 115.8 % (0.19 ± 0.13 to 0.41 ± 0.22, p = 0.033) in state M and 29.0 % in state S (0.31 ± 0.14 to 0.40 ± 0.15, p = 0.219). No arrhythmia was induced in the H models. RA inclusion increased the TCL of LA reentries by 5.5 % (186.9 ± 13.3 ms to 197.2 ± 18.3 ms, p = 0.006) in scenario M and decreased it by 7.2 % (224.3 ± 27.6 ms to 208.2 ± 34.8 ms , p = 0.010) in scenario S. RA inclusion increased LA inducibility revealing 5.5 ± 3.0 new points per patient in the LA for the biatrial model, which did not induce reentry in the monoatrial model. Conclusions LA reentry vulnerability in a biatrial model is higher than in a monoatrial model. Incorporating the RA in patient-specific computational models unmasked potential inducing points in the LA. The RA had a substrate-dependent effect on reentry dynamics, altering the TCL of LA-induced reentries. Our results provide evidence for an important role of the RA in the maintenance and induction of arrhythmia in patient-specific computational models, thus suggesting the use of biatrial models. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860974. This work was supported by the Leibniz ScienceCampus "Digital Transformation of Research" with funds from the programme "Strategic Networking in the Leibniz Association". The authors acknowledge support by the state of Baden-Württemberg through bwHPC. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Subjects provided written informed consent and the study protocol was reviewed and approved by the ethical committee of Guy's Hospital, London, UK, and University Hospital Heidelberg, Heidelberg, Germany I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes A carputils bundle containing the openCARP experiment is publicly available

AIMS:The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalized in silico models on arrhythmia vulnerability. METHODS AND RESULTS:Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalized anatomical atrial models. The Courtemanche M. et al. cellular model was adjusted to reproduce patient-specific ERP. Four modeling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalized approaches (A and B) were based on literature data, while personalized approaches (C and D) were based on patient measurements. Modeling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. Mean vulnerability was 3.4 ± 4.0%, 7.7 ± 3.4%, 9.0 ± 5.1%, and 7.0 ± 3.6% for scenarios A-D, respectively. Mean tachycardia cycle length was 167.1 ± 12.6 ms, 158.4 ± 27.5 ms, 265.2 ± 39.9 ms, and 285.9 ± 77.3 ms for scenarios A-D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50 ms changed the vulnerability of the model to 5.8 ± 2.7%, 6.1 ± 3.5%, 6.9 ± 3.7%, 5.2 ± 3.5%, and 9.7 ± 10.0%, respectively. CONCLUSION:Increased ERP dispersion had a greater effect on re-entry dynamics than on vulnerability. Inducibility was higher in personalized scenarios compared with scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low-voltage areas. Effective refractory period measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalization of atrial in silico models appears essential and requires confirmation in larger cohorts.

Abstract Background 3D imaging and high-resolution electroanatomical mapping have become an integral part of cardiac electrophysiology and the management of patients with arrhythmias. With further technological advances the significance of these modalities continues to grow. However, to perform regional quantitative analyses and intra- and inter-individual, as well as cross-modality comparisons, a universal definition of atrial regions and their boundaries is required. While for the left ventricle there is already an established standardized regionalization (AHA 18-segment model), there is no consensus for the regionalization of the atria. Purpose Here we propose standardized left and right atrial segments based on anatomical, electrophysiological and clinical considerations, with precise definition of regional boundaries allowing for reproducible and automated regionalization. Methods and results In a multidisciplinary task force of the European PersonalizeAF consortium involving cardiologists and cardiac electrophysiologists, as well as specialists in cardiac imaging and computational modeling we developed a standardized regionalization dividing the left atrium into eight, and the right atrium into seven segments (15-segment bi-atrial model) (Fig. 1A). As a proof-of-principle, two software algorithms for automatic regionalization of 3D atrial geometries based on the standardized 15-segment bi-atrial model were developed independently by different working groups of the PersonalizeAF consortium – one based on a commercially available software, the other being open-source. The algorithm based on the commercial software obtains the 15-segment bi-atrial model by dividing the surface mesh in the defined regions along geodesics using the Fast Marching Method (Fig. 1C). For the open-source solution, a publicly available semi-automatic bi-atrial division pipeline was developed: In this algorithm, after standardizing the surface mesh by remeshing and clipping the pulmonary veins, the orifices are annotated automatically. Based on these anatomical landmarks, the boundaries of each region are inferred by calculating geodesics using Dijkstra’s algorithm. Both algorithms were able to annotate the regions with high accuracy and very good agreement as indicated by interrater reliability testing (kappa >0.9), in geometries derived from 50 patients and 2 imaging modalities (CT and MRI), thus demonstrating the universal applicability and reproducibility of the standardized segments. Conclusion We propose a standardized regionalization of the cardiac atria for 3D cardiac imaging, electroanatomical mapping and computational modeling, based on anatomical, electrophysiological and clinical considerations. The reproducibility and universal applicability of this 15-segment bi-atrial model was demonstrated by two independently developed software algorithms for automatic regionalization.

Efficient personalized ablation strategies for treating atrial arrhythmias remain challenging. Discrepancies in identifying arrhythmogenic areas using characterization methods, such as late gadolinium enhanced magnetic resonance imaging (LGE-MRI) and electroanatomical mapping, require a comparative analysis of local impedance (LI) and LGE-MRI data. This study aims to analyze correlations as basis for improvement of treatment strategies. 16 patients undergoing left atrium (LA) ablation with LGE-MRI acquisition and LI data recording were recruited. LGE-MRI data and LI measurements were normalized to patient- and modality-specific blood pool references. A global mean shape was generated based on all patient geometries, and normalized local impedance (LIN) and LGE-MRI image intensity ratio (IIR) data points were coregistered for comparison. Data analysis comprised intra-patient and inter-patient assessments, evaluating differences in LIN values among datasets categorized by their IIR. Due to substantial deviations in LIN values, even within the same patient and IIR-category, discerning the presence or absence of a correlation was challenging, and no statistically significant correlation could be identified. Our findings underscore the necessity for standardized protocols in data acquisition, processing, and comparison, to minimize unquantified confounding effects. While immediate substitution of LI for LGE-MRI seems improbable given the significant LIN variations, this preliminary study lays the groundwork for systematic data acquisition. By ensuring data quality, a meaningful comparison between LI and LGE-MRI data can be facilitated, potentially shaping future strategies for atrial arrhythmia treatment.

Background: Electrical impedance measurements have become an accepted tool for monitoring intracardiac radio frequency ablation. Recently, the long-established generator impedance was joined by novel local impedance measurement capabilities with all electrical circuit terminals being accommodated within the catheter. Objective: This work aims at in silico quantification of distinct influencing factors that have remained challenges due to the lack of ground truth knowledge and the superposition of effects in clinical settings. Methods: We introduced a highly detailed in silico model of two local impedance enabled catheters, namely IntellaNav MiFi OI and IntellaNav Stablepoint, embedded in a series of clinically relevant environments. Assigning material and frequency specific conductivities and subsequently calculating the spread of the electrical field with the finite element method yielded in silico local impedances. The in silico model was validated by comparison to in vitro measurements of standardized sodium chloride solutions. We then investigated the effect of the withdrawal of the catheter into the transseptal sheath, catheter-tissue interaction, insertion of the catheter into pulmonary veins, and catheter irrigation. Results: All simulated setups were in line with in vitro experiments and in human measurements and gave detailed insight into determinants of local impedance changes as well as the relation between values measured with two different devices. Conclusion: The in silico environment proved to be capable of resembling clinical scenarios and quantifying local impedance changes. Significance: The tool can assists the interpretation of measurements in humans and has the potential to support future catheter development.

Atrial fibrillation (AF) is the most common sustained arrhythmia posing a significant burden to patients and leading to an increased risk of stroke and heart failure. Additional ablation of areas of arrhythmogenic substrate in the atrial body detected by either late gadolinium enhancement magnetic resonance imaging (LGE-MRI) or electroanatomical mapping (EAM) may increase the success rate of restoring and maintaining sinus rhythm compared to the standard treatment procedure of pulmonary vein isolation (PVI). To evaluate if LGE-MRI and EAM identify equivalent substrate as potential ablation targets, we divided the left atrium (LA) into six clinically important regions in ten patients. Then, we computed the correlation between both modalities by analyzing the regional extents of identified pathological tissue. In this regional analysis, we observed no correlation between late gadolinium enhancement (LGE) and low voltage areas (LVA), neither in any region nor with regard to the entire atrial surface (-0.3

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska- Curie grant agreement Introduction Although the effective refractory period (ERP) is one of the main electrophysiological properties governing atrial tachycardia (AT) maintenance, ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision making. State-of-the-art models usually do not consider physiological ERP gradients but assume a homogeneous ERP distribution. This assumption might have an influence on the ability to induce reentries in the model. Aim To evaluate the impact of incorporating clinical ERP measurements when creating in silico personalized models to predict vulnerability to atrial fibrillation (AF). Methods Clinical ERP measurements were obtained from three patients from multiple locations in the atria. The protocol for ERP identification consisted of trains of 7 S1 stimuli with a basic cycle length of 500ms followed by an S2 stimulus with a coupling interval between 300 and 200ms in decrements of 10ms until loss of capture. The atrial geometries from the electroanatomical mapping system were used to generate personalized atrial models. To reproduce patient-specific ERP, the established Courtemanche cellular model was gradually reparameterized from control conditions to a setup representing AF-induced remodeling. Three different approaches were studied: 1) a control scenario with no ERP personalization 2) a discrete split where each region had a single ERP value and 3) a continuous ERP distribution by interpolation of measured ERP data (Fig. 1). Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different locations separated by 3cm. The number and location of inducing points and type of arrhythmia were determined for the three approaches. The mean conduction velocity was set to 0.7 m/s and the electrical propagation in the atria was modeled by the monodomain equation and solved with openCARP. Results Incorporating patient-specific ERP as a continuous distribution did not induce any reentrant activity. A summary of induced ATs is shown in Table 1. For patient A, AF was induced from 3 different locations with the control setup, whereas 9 ATs were induced with the regional method, of which 4 were AF and 5 macro reentries. For patient B, AF was induced from 1 point with the control setup; whereas with the regional approach, AF was induced at 4 points. For patient C, only one macro reentry was induced with the regional method. Conclusion Incorporation of patient-specific ERP values has an impact on the assessment of AF vulnerability. Furthermore, the type of personalization affects the likelihood of AF inducibility. The incorporation of more detailed ERP distributions may lead to a more accurate prediction of AF trigger points and could in the future inform patient-specific therapy planning. Larger cohorts need to follow to demonstrate the role of incorporating clinical patient-specific ERP values into personalized models for predicting AF vulnerability.

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.

Objective: 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. Methods: We compared action potential durations, local activation times (LATs), and electrocardiograms (ECGs) for sinus rhythm simulations on healthy and fibrotically infiltrated atrial models. Results: All simplified model solutions yielded LATs and P 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 >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 wave amplitudes in the precordial leads. Conclusion: 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. Significance: 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.

Atrial fibrillation (AF) is one of the most common cardiac diseases. However, a complete understanding of how to treat patients suffering from AF is still not achieved. As the isolation of the pulmonary veins in the left atrium (LA) is the standard treatment for AF, the role of the right atrium (RA) in AF is rarely considered. We investigated the impact of including the RA on arrhythmia vulnerability in silico. We generated a dataset of five mono-atrial (LA) and five bi-atrial models with three different electrophysiological (EP) setups each, regarding different states of AF-induced remodelling. For every model, a pacing protocol was run to induce reentries from a set of stimulation points. The average share of inducing points across all EP setups was 0.0, 0.8 and 6.7% for the mono-atrial scenario, 0.5, 27.3 and 37.9% for the biatrial scenario. The increase in inducibility of LA stimulation points from mono- to bi-atrial scenario was 0.91 ± 2.03%, 34.55 ± 14.9% and 44.2 ± 14.9 %, respectively. In this study, the RA had a marked impact on the results of the vulnerability assessment that needs to be further investigated.

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 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.

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.

Regions with pathologically altered substrate have been identified as potentially proarrhythmic for atrial fibrillation. Mapping techniques, such as voltage mapping, are currently used to estimate the location of these fibrotic areas. Recently, local impedance (LI) has gained attention as another modality for atrial substrate assessment as it does not rely on the dynamically changing electrical activity of the heart. However, its limits for assessing non-transmural and complex fibrosis patterns have not yet been studied in detail. In this work, the ability of EGMs and LI to identify non-transmural fibrosis at different transmural levels using in silico experiments is explored. A pseudo-bidomain model was used to recover the extracellular potential on the surface of the tissue while LI reconstruction was calculated by a time-difference imaging approach with an homogeneous tissue background conductivity. Four fibrosis configurations were modeled to compare the two modalities using Pearson correlation coefficient. Only one transmural structure was detected by voltage whereas non-transmural structures, namely endo-, midmyo-, and epicardial, yielded zero. The correlation for LI maps ranged from -0.02 to 0.74. We conclude that LI, together with EGMs, can be expected to distinguish between healthy and fibrotic tissue, paving the way towards its use as a surrogate for non-transmural atrial substrate.