This paper explores the requirements for humancentered artificial intelligence (AI) tools for heart failure (HF) management, focusing on the needs of diverse healthcare settings in selected European countries (Netherlands, Spain, Czech Republic) and a Latin American country (Peru). Clinicians, patients, ethicists, and technical experts were engaged through cocreation workshops, local groups, narrative interviews, and surveys to gather clinical, ethical, and regulatory requirements for AI implementation in HF care. These activities provided input on the intended clinical use of AI tools, as well as patient data privacy and security concerns. Clinical requirements revealed regional differences in AI tool preferences and key predictors. European clinicians favored integration into secondary and tertiary care, focusing on quality of life and comprehensive follow-up measures, while clinicians in Peru prioritized secondary care with an emphasis on treatment adherence and complication management. Ethical considerations, such as data privacy and bias mitigation, were universally important but some context-specific differences emerged. European stakeholders emphasized mitigating biases related to sex, ethnicity, and socioeconomic status under European regulations, whereas Latin American stakeholders focused on context-specific ethics and robust national oversight. By aligning these insights with FUTURE-AI principles, the study ensures the development of effective, human-centered AI tools. This research highlights the importance of continuous stakeholder engagement and contextualizing AI applications to enhance their relevance, usability, and adoption across diverse healthcare settings.

Abstract Background In current athletes’ electrocardiography (ECG) guidelines, the presence of ECG findings meeting left ventricular hypertrophy (LVH) criteria is interpreted as normal variation. However, this is largely based on echocardiographic studies in men. The efficacy of LVH criteria, in both sexes, obtained by advanced ECG analysis tools validated against cardiac magnetic resonance imaging (CMR) remains largely unknown. Purpose To evaluate diagnostic accuracy of ECG-LVH criteria in detecting CMR-defined LVH among elite athletes, with a specific focus on sex differences and potential correlations between ECG-LVH criteria measurements and myocardial mass and thickness. Methods We analysed 12-lead ECG and CMR data in 251 healthy, elite athletes (43% female, median age 25 [interquartile range: 22-30]) from the ELITE cohort. ECG-LVH criteria (Sokolow-Lyon, Cornell, Peguero Lo-Presti, Modified sum of 12 leads voltages and product, Romhilt-Estes points) were quantified using a digitized modular ECG analysis system. Our primary metric of interest was area under he curve (AUC) for each criterion as predictor for LVH on CMR. CMR-LVH was defined as exceeding population sex-specific 95th percentile thresholds for indexed left ventricular mass (LVM) and maximum wall thickness (LVWTmax). Furthermore, we examined correlations (Pearson's r, multivariate regression) between voltages/points within each criterion and LVM or LVWTmax. Results Female athletes (n=107) showed higher AUCs for ECG-LVH criteria in detecting LVM compared to male athletes (n=144) (average 0.66; 95% confidence interval [CI]: 0.53-0.79 vs. 0.52; 95% CI: 0.33-0.70, respectively). Notably, Sokolow-Lyon (0.74; 95%CI: 0.63-0.85) and Peguero Lo-Presti criteria (0.71; 95%CI: 0.59-0.83) had acceptable performance in female athletes in identifying increased LVM and performed better than in male athletes (0.50; 95%CI 0.34-0.66; p=0.011, and 0.48; 95%CI 0.32-0.63; p=0.015, respectively). In both sexes, ECG-LVH criteria showed similarly poor discriminating ability in detecting increased LVWTmax (average AUCs 0.58; 95%CI: 0.35-0.80 for females; 0.53; 95%CI: 0.41-0.64 for males). ECG-LVH criteria voltages were more strongly correlated with LVM in female athletes than in males with the Modified 12-lead voltage product showing the strongest correlation in both (r = 0.45 vs. 0.17; p=0.020, respectively [Figure 2]). Multivariate regression analysis identified this criterion as a predictor of LVM after adjusting for sex, endurance sports participation and heart rate (p<0.001). Conclusion ECG-LVH criteria, even with digitized QRS voltage analysis, demonstrate limited efficacy in predicting CMR-derived maximum wall thickness beyond conventional LVH thresholds. We found clear sex differences, as several criteria, particularly Sokolow-Lyon, demonstrated acceptable performance in detecting increased LVM in female athletes. Our findings highlight the need for sex-specific ECG criteria in elite athletes. ROC curves. Correlation analysis.

Background Electrocardiographic abnormalities are common in arrhythmogenic right ventricular cardiomyopathy and are included in the 2010 Task Force Criteria. Their time course, however, remains uncertain. In this retrospective observational study, we aimed to assess the long‐term evolution of electrocardiographic characteristics and their relation to ventricular arrhythmias. Methods and Results Three hundred fifty‐three patients with arrhythmogenic right ventricular cardiomyopathy as per the 2010 Task Force Criteria with 6871 automatically processed 12‐lead digital ECGs were included. The relationship between the electrocardiographic parameters and the risk of ventricular arrhythmias was assessed at 10 years from the first ECG. Electrocardiographic parameters were compared between the first contact ECG, the ECG at diagnosis, and the most recent ECG. Median time between the first and the latest ECG was 6 [interquartile range, 1–14] years. Reductions of QRS voltage, R‐ and T‐wave amplitudes between the first, diagnostic, and the latest ECGs were observed across precordial and extremity leads. Mean QRS duration increased from 96 to 102 ms ( P <0.001), terminal activation duration (V 1 ) from 47 to 52 ms ( P <0.001), and QTc from 419 to 432 ms ( P <0.001). T‐wave inversions in leads V 3 to V 6 and aVF at first ECG were associated with ventricular arrhythmias (adjusted hazard ratio [HR adj ][V 3 ], 2.03 [95% CI, 1.23–3.34] and HR adj [aVF], 1.87 [95% CI, 1.13–3.08]). Conclusions Depolarization and repolarization parameters evolved over time in patients with arrhythmogenic right ventricular cardiomyopathy, supporting the progressive nature of arrhythmogenic right ventricular cardiomyopathy. Electrocardiographic abnormalities may be detected before diagnosis and might, although not fulfilling the 2010 Task Force Criteria, be markers of early disease. T‐wave inversion in leads V 3 or aVF before diagnosis was associated with ventricular arrhythmias during follow‐up.

Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.

IntroductionThe European Health Data Space (EHDS) initiative was launched to create a unified framework for health data exchange across Europe. Central to this initiative is the European Electronic Health Record Exchange Format, designed to achieve interoperability of electronic health record data across Europe. Despite these advancements, the readiness of current guidelines and implementations, such as the European Patient Summary, to support secondary use in clinical research, particularly in cardiology, remains underexplored.MethodsThis study aims to evaluate the European Patient Summary guidelines and their implementations, specifically the HL7 FHIR International Patient Summary Implementation Guide, to determine their suitability for secondary use in clinical research. The focus is on identifying gaps and extensions needed to enhance the utility of the European Patient Summary for building artificial intelligence models in assisting heart failure management.ResultsWe selected two European Union-funded research projects, DataTools4Heart and AI4HF, that aim to reuse electronic health record data to develop artificial intelligence models for personalized decision support services for heart failure patients. We analyzed their clinical use cases and the specific data items required, and we compared these with the current European Patient Summary guidelines and provided a detailed gap analysis indicating similarities and required extensions. In our gap analysis, we also compared the needs of DataTools4Heart and AI4HF with the HL7 FHIR International Patient Summary Implementation Guide to assess the extensions needed to support clinical research.DiscussionThe EHDS is a transformative initiative to establish a European health data ecosystem that supports healthcare delivery and clinical research. Our comparative analysis demonstrates that, with minor extensions, these guidelines have significant potential to facilitate access to electronic healthcare record data for the secondary use, particularly in training AI models. We advocate for the adoption of an International Patient Summary format as a semantically interoperable core set of data elements, which will enhance global clinical research efforts and improve patient outcomes through precision medicine.

Background Electrical activity underlying the T-wave is less well understood than the QRS-complex. This study investigated the relationship between normal T-wave morphology and the underlying ventricular repolarization gradients using the equivalent dipole layer (EDL).Methods Body-surface-potential-maps (BSPM, 67-leads) were obtained in nine normal cases. Subject specific MRI-based anatomical heart/torso-models with electrode positions were created. The boundary element method was used to account for the volume conductor effects. To simulate the measured T-waves, the EDL was used to apply different ventricular repolarization gradients: a) transmural, b) interventricular c) apico-basal and d) all three gradients (a-c) combined. The combined gradient (d) was optimized using an inverse procedure (Levenberg-Marquardt). Correspondence between simulated and measured T-waves was assessed using correlation coefficient (CC) and relative difference (RD).Results Realistic T-waves were simulated if repolarization times of: (a) the epicardium were smaller than the endocardium; (b) the left ventricle were smaller than the right ventricle and (c) the apex increased towards the base. The apico-basal gradient resulted in the highest correspondence between measured and simulated T-waves (CC = 0.84(0.81-0.91);RD = 0.68(0.60-0.71)) compared to a transmural gradient (CC = 0.77(0.71-0.80);RD = 1.46(0.82-1.75)) and an interventricular gradient (CC = 0.71(0.67-0.80);RD = 0.85(0.75-0.87)). All three gradients combined further improved the correspondence between measured and simulated T-waves (CC = 0.83 (0.82-0.89);RD = 0.60(0.51-0.63)), especially after optimization (CC = 0.96(0.94-0.98);RD = 0.27(0.22-0.34)).Conclusion The application of all repolarization gradients combined resulted in the largest agreement between simulated and measured T-waves, followed by the apico-basal repolarization gradient. With these findings, we will optimize our EDL-based inverse procedure to assess repolarization abnormalities.

The electrical activity underlying the T-wave is less well understood compared to the QRS complex. In this study we aim to investigate the relationship between T-wave morphology and the underlying ventricular repolarization gradients using the equivalent dipole layer (EDL). Body-surface-potential-maps (67-leads) were obtained in nine control subjects. Subject specific CT/MRI-based anatomical heart/torso models with electrode positions were created. The boundary element method was used to compute the transfer matrix to account for the volume conductor effects. The source strength at each ventricular node of the EDL was defined by the shape of the transmembrane potential (TMP). A new template for the TMP was created and different slopes were tested for the plateau phase of the TMP. Three ventricular gradients were applied: a) transmural, b) interventricular c) apicobasal and d) combined. Realistic T-waves could be simulated for all three ventricular repolarization gradients with the apico-basal gradient resulting in the best fit. Combination of all three gradients further improved the match between measured and simulated T-waves, indicating that all three gradients are required in the genesis of the T-wave. The knowledge obtained in this study will be used to optimize the initial estimate in our EDL based inverse procedure.

Abstract Aims In Brugada syndrome (BrS), with spontaneous or ajmaline-induced coved ST elevation, epicardial electro-anatomic potential duration maps (epi-PDMs) were detected on a right ventricle (RV) outflow tract (RVOT), an arrhythmogenic substrate area (AS area), abolished by epicardial-radiofrequency ablation (EPI-AS-RFA). Novel CineECG, projecting 12-lead electrocardiogram (ECG) waveforms on a 3D heart model, previously localized depolarization forces in RV/RVOT in BrS patients. We evaluate 12-lead ECG and CineECG depolarization/repolarization changes in spontaneous type-1 BrS patients before/after EPI-AS-RFA, compared with normal controls. Methods and results In 30 high-risk BrS patients (93% males, age 37 + 9 years), 12-lead ECGs and epi-PDMs were obtained at baseline, early after EPI-AS-RFA, and late follow-up (FU) (2.7–16.1 months). CineECG estimates temporo-spatial localization during depolarization (Early-QRS and Terminal-QRS) and repolarization (ST-Tpeak, Tpeak-Tend). Differences within BrS patients (baseline vs. early after EPI-AS-RFA vs. late FU) were analysed by Wilcoxon signed-rank test, while differences between BrS patients and 60 age–sex-matched normal controls were analysed by the Mann–Whitney test. In BrS patients, baseline QRS and QTc durations were longer and normalized after EPI-AS-ATC (151 ± 15 vs. 102 ± 13 ms, P < 0.001; 454 ± 40 vs. 421 ± 27 ms, P < 0.000). Baseline QRS amplitude was lower and increased at late FU (0.63 ± 0.26 vs. 0.84 ± 13 ms, P < 0.000), while Terminal-QRS amplitude decreased (0.24 ± 0.07 vs. 0.08 ± 0.03 ms, P < 0.000). At baseline, CineECG depolarization/repolarization wavefront prevalently localized in RV/RVOT (Terminal-QRS, 57%; ST-Tpeak, 100%; and Tpeak-Tend, 61%), congruent with the AS area on epi-PDM. Early after EPI-AS-RFA, RV/RVOT localization during depolarization disappeared, as Terminal-QRS prevalently localized in the left ventricle (LV, 76%), while repolarization still localized on RV/RVOT [ST-Tpeak (44%) and Tpeak-Tend (98%)]. At late FU, depolarization/repolarization forces prevalently localized in the LV (Terminal-QRS, 94%; ST-Tpeak, 63%; Tpeak-Tend, 86%), like normal controls. Conclusion CineECG and 12-lead ECG showed a complex temporo-spatial perturbation of both depolarization and repolarization in BrS patients, prevalently localized in RV/RVOT, progressively normalizing after epicardial ablation.

Introduction: Inherited cardiomyopathies are associated with a broad spectrum of potentially lethal phenotypes characterized by structural and electrical myocardial remodeling. Increased awareness and genetic cascade screening lead to more genotype-positive, yet phenotype-negative individuals to be evaluated and followed up. The predictive value of genetic testing is hampered by incomplete penetrance and high variability in disease onset, progression and severity. Clinical challenges: Dilated cardiomyopathy usually manifests with symptoms of heart failure and ventricular arrhythmias (VA) develop in advanced disease. In arrhythmogenic cardiomyopathy (ACM), electrical remodeling can precede structural and functional changes and life-threatening VA can be the first disease manifestation. Early signs and symptoms may be subtle and go unnoticed. Physicians are in great need of appropriate screening and risk-stratification strategies. Task Force Criteria (TFC) were established to standardize the clinical diagnosis of ACM but risk-stratification remains challenging. Accurate prediction of disease progression in variation carriers is currently beyond the capabilities of diagnostic tests. Proposed diagnostic techniques: We propose three ECG-based techniques; isopotential mapping, inverse ECG and CineECG, to enhance risk-stratification in ACM. With the use of isopotential mapping abnormal spatio-temporal activation and repolarization may be identified. Furthermore, by combining subject specific >= 12-lead ECG data with cardiothoracic imaging using inverse ECG techniques, the direct link between ECG and cardiac anatomy can be obtained. Conclusion: New ECG techniques may prove more sensitive to detect early de- and repolarization abnormalities in yet asymptomatic variation carriers. Early electrical signs of disease progression may be identified prior to symptoms. Furthermore, individualized risk-stratification may be enhanced.

The electrocardiogram (ECG) plays an important role in systematically assessing cardiac electrical function, but the standard 12-lead ECG only provides only a distant view on cardiac electrical activity. Using non-invasive inverse ECG techniques, additional detailed information on cardiac electrical activity can be obtained by linking cardiac electrical activity to anatomy to enable the identification of subtle disease progression in arrhythmogenic cardiomyopathy. Arrhythmogenic cardiomyopathy is characterized by structural and electrical myocardial remodeling and can manifest as a broad range of lethal phenotypes. In arrhythmogenic cardiomyopathy, electrical remodeling can precede structural and functional changes and sudden cardiac death can be the first disease manifestation. This highlights the need for accurate screening and risk-stratification strategies. The first part of this thesis focusses on describing the optimization of a traditional inverse ECG technique to provide non-invasive insight in endocardial and epicardial cardiac electrical activity by combining 67-lead ECG data with patient specific CT/MRI-based anatomical models. To be able to identify early signs of arrhythmogenic cardiomyopathy development, accurate imaging of sinus rhythm is of importance. Therefore, in Chapter 2, we report on our work regarding the optimization of the inverse ECG technique for the estimation of sinus rhythm and report on its performance (Chapter 3) by comparing it to invasive local activation maps. With the incorporation of a subject-specific anatomy-based model of the His-Purkinje system a physiologically realistic and robust estimation of the ventricular activation sequence is obtained. The optimized inverse ECG technique detected local electrophysiological characteristics in the activation sequence in pathogenic variant carriers with and without any clinical signs of disease (Chapter 4). To further optimize the performance of the inverse ECG technique by developing a new method to model myocardial disease in ECG simulation in Chapter 5 to provide a realistic relation between ECG waveforms and underlying activation sequences. As traditional inverse ECG techniques are mathematically complex and computationally demanding, we focus on CineECG, a new method to image key features of the activation sequence that are difficult to reliably obtain from the ECG. We conceptually validated the technique in cases of bundle branch blocks (Chapter 6) and after evaluation, the method was optimized and validated through a simulation study (Chapter 7). As accurate assessment of subtle ECG changes is limited due to inconsistencies in electrode positioning, we focused on the optimization of the 12-lead ECG acquisition by introducing a 3D-camera based method to reduce electrode placement misplacement (Chapter 8). With this new technique, the identification of subtle changes in the QRS complex during arrhythmogenic cardiomyopathy follow-up may be improved. In Chapter 9, we describe how novel AI-based algorithms may aid current clinical practice together with its potential benefits and challenges. With such algorithms, the complex nature of disease progression in arrhythmogenic cardiomyopathy may be further unraveled. To conclude the thesis, the application of techniques presented in this thesis to enhance diagnosis and risk-stratification in arrhythmogenic cardiomyopathy is described (Chapter 10). The techniques are viewed within the context of possible fields of application in current clinical practice.

Background:Portable, smartphone-sized electrocardiography (ECG) has the potential to reduce time to treatment for patients suffering acute cardiac ischemia, thereby lowering the morbidity and mortality. In the UMC Utrecht, a portable, smartphone-sized, multi-lead precordial ECG recording device (miniECG 1.0, UMC Utrecht) was developed. Objectives:The purpose of this study was to investigate the ability of the miniECG to capture ischemic ECG changes in a porcine coronary occlusion model. Methods:In 8 animals, antero-septal myocardial infarction was induced by 75-minute occlusion of the left anterior descending artery, after the first or second diagonal. MiniECG and 12-lead ECG recordings were acquired simultaneously before, during and after coronary artery occlusion and ST-segment deviation was evaluated. Results:During the complete occlusion and reperfusion period, miniECG showed large ST-segment deviation in comparison to 12-lead ECG. MiniECG ST-segment deviation was observed within 1 minute for most animals. The miniECG was positive for ischemia (ie, ST-segment deviation ≥1 mm) for 99.7% (Q1-Q3: 99.6%-99.9%) of the occlusion time, while the 12-lead was only positive for 79.8% (Q1-Q3: 81.1%-98.7%) of the time (P = 0.018). ST-segment deviation reached maxima of 10.5 mm [95% CI: 6.5-14.5 mm] vs 5.0 mm [95% CI: 2.0-8.0 mm] for the miniECG vs 12-lead ECG, respectively. Conclusions:MiniECG ST-segment deviation was observed early and was of large magnitude during 75 minutes of porcine transmural antero-septal infarction. The miniECG was positive for ischemia for the complete occlusion period. These findings demonstrate the potential of the miniECG in the detection of cardiac ischemia. Although clinical research is required, data suggests that the miniECG is a promising tool for the detection of cardiac ischemia.

Aims Patients with non-ischemic dilated cardiomyopathy (DCM) are at considerable risk for end-stage heart failure (HF), requiring close monitoring to identify early signs of disease. We aimed to develop a model to predict the 5-years risk of end-stage HF, allowing for tailored patient monitoring and management. Methods and results Derivation data were available from a Dutch cohort of 293 DCM patients, with external validation available from a Czech Republic cohort of 235 DCM patients. Candidate predictors spanned patient and family histories, ECG and echocardiogram measurements, and biochemistry. End-stage HF was defined as a composite of death, heart transplantation, or implantation of a ventricular assist device. Lasso and sigmoid kernel support vector machine (SVM) algorithms were trained using cross-validation. During follow-up 65 (22%) of Dutch DCM patients developed end-stage HF, with 27 (11%) cases in the Czech cohort. Out of the two considered models, the lasso model (retaining NYHA class, heart rate, systolic blood pressure, height, R-axis, and TAPSE as predictors) reached the highest discriminative performance (testing c-statistic of 0.85, 95%CI 0.58; 0.94), which was confirmed in the external validation cohort (c-statistic of 0.75, 95%CI 0.61; 0.82), compared to a c-statistic of 0.69 for the MAGGIC score. Both the MAGGIC score and the DCM-PROGRESS model slightly over-estimated the true risk, but were otherwise appropriately calibrated. Conclusion We developed a highly discriminative risk-prediction model for end-stage HF in DCM patients. The model was validated in two countries, suggesting the model can meaningfully improve clinical decision-making.

Abstract Aims Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a progressive inherited cardiac disease. Early detection of disease and risk stratification remain challenging due to heterogeneous phenotypic expression. The standard configuration of the 12 lead electrocardiogram (ECG) might be insensitive to identify subtle ECG abnormalities. We hypothesized that body surface potential mapping (BSPM) may be more sensitive to detect subtle ECG abnormalities. Methods and results We obtained 67 electrode BSPM in plakophilin-2 (PKP2)-pathogenic variant carriers and control subjects. Subject-specific computed tomography/magnetic resonance imaging based models of the heart/torso and electrode positions were created. Cardiac activation and recovery patterns were visualized with QRS- and STT-isopotential map series on subject-specific geometries to relate QRS-/STT-patterns to cardiac anatomy and electrode positions. To detect early signs of functional/structural heart disease, we also obtained right ventricular (RV) echocardiographic deformation imaging. Body surface potential mapping was obtained in 25 controls and 42 PKP2-pathogenic variant carriers. We identified five distinct abnormal QRS-patterns and four distinct abnormal STT-patterns in the isopotential map series of 31/42 variant carriers. Of these 31 variant carriers, 17 showed no depolarization or repolarization abnormalities in the 12 lead ECG. Of the 19 pre-clinical variant carriers, 12 had normal RV-deformation patterns, while 7/12 showed abnormal QRS- and/or STT-patterns. Conclusion Assessing depolarization and repolarization by BSPM may help in the quest for early detection of disease in variant carriers since abnormal QRS- and/or STT-patterns were found in variant carriers with a normal 12 lead ECG. Because electrical abnormalities were observed in subjects with normal RV-deformation patterns, we hypothesize that electrical abnormalities develop prior to functional/structural abnormalities in ARVC.

We present the use of CineECG in visualizing abnormal ventricular activation in a case of a complex conduction disorder. CineECG combines the standard 12‑lead surface ECG with a 3D anatomical model of the heart. It projects the location and direction of the average ventricular activation and recovery on the heart model over time. In this case, CineECG was able to visualize the different type of fascicular conduction in this progressive conduction block. This novel imaging technique was able to provide additional insight in this complex case, and might be of use in other complex ECG patterns.

Electrocardiographic imaging (ECGI) is a promising tool for the treatment and diagnosis of cardiac arrhythmias. ECGI estimates non-invasively the electrical activity of the heart using body surface potentials (BSPs) obtained at the body surface in combination with a specific CT/MRI based anatomical models and defined electrode positions. In order to solve the ECGI inverse problem the first step to be considered is indeed the image segmentation and mesh generation.

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Dutch Heart Foundation Introduction Arrhythmogenic cardiomyopathy (ACM) is a heterogeneous progressive disease. Identification of patients at risk for malignant ventricular arrhythmias is challenging, making extensive cardiac follow-up necessary. CineECG provides insight in the average cardiac pathway of cardiac electrical activity. In previous studies, CineECG proved useful to detect disease progression. Objective Evaluate the applicability of CineECG to monitor disease progression in plakophilin-2 (PKP2) pathogenic mutation carriers. Methods To compute the CineECG, a 3D heart/torso model and 12 lead ECG is used. From 68 PKP2 pathogenic mutation carriers, all raw ECGs were extracted from the patient database. In pathogenic mutation carriers with definite ACM, the ECG ±2 years before (ECG1), at (ECG2) and ±2 years after (ECG3) diagnosis were selected. In pathogenic mutation carriers without definite ACM, the most recent ECG (ECG2) and the ECG ±2 years before (ECG1) were selected. CineECGs were computed for the QRS complex and the distance between CineECG location at end QRS was determined per subject for subsequent CineECGs. Results In 53 pathogenic mutation carriers ≥2 ECGs were available. 33 pathogenic mutation carriers were diagnosed with definite ACM of whom 4 had an ECG before, at and after diagnosis. Average distance between CineECG location at end QRS was 7.8±6.8 mm. In pathogenic mutation carriers with definite ACM, CineECG before and at diagnosis (figure, example 1&2) were different whereas CineECG at and after diagnosis did not always change. In pathogenic mutation carriers without definite ACM, in 14/19 changes in CineECG were observed (figure, example 3), whereas in the others (figure, example 4) not. Conclusion Our preliminary results show that CineECG provides additional insight in the changes of cardiac activation in ACM patients and may enable detection of disease progression. Further analysis will also include cardiac repolarization.

The relation between abnormal ventricular activation and corresponding ECGs still requires additional understanding. The presence of disease breaks the equivalence in equivalent dipole layer-based $ECG$ simulations. In this study, endocardial and epicardial patches were introduced to simulate abnormal wave propagation in different types of substrates. The effect of these different types of substrates on the $QRS$ complex was assessed using a boundary element method forward $heart/torso$ and a 64-lead body surface potential map (BSPM). Activation was simulated using the fastest route algorithm with six endocardial foci and $QRS$ complexes corresponding to abnormal patch activation were compared to the $QRS$ complexes of normal ventricular activation using correlation coefficient $(CC)$ . Abnormal patch activation affected both $QRS$ morphology and duration. These $QRS$ changes were observed in different leads, depending on substrate location. With insights obtained in such simulations, risk-stratification and understanding of disease progression may be further enhanced.

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Dutch Heart Foundation Introduction We recently optimized our ECG imaging (ECGi) method for the estimation of endo- and epicardial activation during sinus rhythm. In patients with arrhythmogenic cardiomyopathy, late gadolinium enhancement (LGE)-CMR can identify regional myocardial injury and the combination of structural and electrical information may provide valuable insight in disease progression and risk stratification. However, the effect of structural disease and local conduction delay on the ECGi estimation of ventricular activation has not been studied. Purpose Evaluate the relation between LGE-CMR and non-invasively estimated local conduction velocity (CV). Methods 8 pathogenic mutation carriers (PKP2/PLN) underwent LGE-CMR for clinical follow up and 67 lead body surface mapping. Subject specific triangulated surface heart/torso/lung meshes were created. ECGi activation sequences were used to determine local CV with the triangulation method. The LGE location was identified according to the AHA 17 segment model. Per segment, variation in CV was computed and local activation timing maps and CV maps were constructed. Results Isochronal crowding was observed in subjects in segments with LGE (figure, red boxes) and locally, conduction velocity decreased. Variation in conduction velocity per segment in subjects with extensive LGE presence (>9 segments) was higher 0.031±0.018 vs. 0.026±0.013 m/s/cm2 in subjects without. Conclusion Our preliminary results indicate the ability of the ECGi method to identify regions with higher variation in local CV. This increase in CV variability might be used to assess the vulnerability to cardiac arrhythmia. Analysis will be extended towards the RV and subsequently, more subjects will be included.

Segmentation of patient-specific anatomical models is one of the first steps in Electrocardiographic imaging (ECGI). However, the effect of segmentation variability on ECGI remains unexplored. In this study, we assess the effect of heart segmentation variability on ECG simulation. We generated a statistical shape model from segmentations of the same patient and generated 262 cardiac geometries to run in an ECG forward computation of body surface potentials (BSPs) using an equivalent dipole layer cardiac source model and 5 ventricular stimulation protocols. Variability between simulated BSPs for all models and protocols was assessed using Pearson's correlation coefficient (CC). Compared to the BSPs of the mean cardiac shape model, the lowest variability (average CC = 0.98 ± 0.03) was found for apical pacing whereas the highest variability (average CC = 0.90 ± 0.23) was found for right ventricular free wall pacing. Furthermore, low amplitude BSPs show a larger variation in QRS morphology compared to high amplitude signals. The results indicate that the uncertainty in cardiac shape has a significant impact on ECGI.