Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.

Objective. Electrocardiographic imaging (ECGI) is a functional imaging modality that consists of two related problems, the forward problem of reconstructing body surface electrical signals given cardiac bioelectric activity, and the inverse problem of reconstructing cardiac bioelectric activity given measured body surface signals. ECGI relies on a model for how the heart generates bioelectric signals which is subject to variability in inputs. The study of how uncertainty in model inputs affects the model output is known as uncertainty quantification (UQ). This study establishes develops, and characterizes the application of UQ to ECGI. Approach. We establish two formulations for applying UQ to ECGI: a polynomial chaos expansion (PCE) based parametric UQ formulation (PCE-UQ formulation), and a novel UQ-aware inverse formulation which leverages our previously established ‘joint-inverse’ formulation (UQ joint-inverse formulation). We apply these to evaluate the effect of uncertainty in the heart position on the ECGI solutions across a range of ECGI datasets. Main results. We demonstrated the ability of our UQ-ECGI formulations to characterize the effect of parameter uncertainty on the ECGI inverse problem. We found that while the PCE-UQ inverse solution provided more complex outputs such as sensitivities and standard deviation, the UQ joint-inverse solution provided a more interpretable output in the form of a single ECGI solution. We find that between these two methods we are able to assess a wide range of effects that heart position variability has on the ECGI solution. Significance. This study, for the first time, characterizes in detail the application of UQ to the ECGI inverse problem. We demonstrated how UQ can provide insight into the behavior of ECGI using variability in cardiac position as a test case. This study lays the groundwork for future development of UQ-ECGI studies, as well as future development of ECGI formulations which are robust to input parameter variability.

Predictive models and computational simulations of cardiac electrophysiology depend on precise anatomical representations, including the local myocardial fibre structure. However, obtaining patient‐specific fibre information is challenging. In addition, the influence of physiological variability in fibre orientation on cardiac activation simulations is poorly understood. We implemented rule‐based algorithms to generate fibres and robust uncertainty quantification methods to determine model output variability with respect to ventricular activation sequences. We used polynomial chaos, which reduces computational demands by using an emulator to approximate the underlying forward model. Our study examined activation sequences in response to nine stimuli and five metrics quantifying essential features of the activation sequence. The results indicated that the primary fibre orientation impacts the overall spread of activation, which could impact more complex patterns of activation; however, there is minimal impact on the location of discrete activation features, such as breakthrough sites. For free wall stimuli, the standard deviation (STD) was highest near the stimulus site, diminishing with distance. Apical stimuli showed complementary STD patterns, with epicardial pacing maximizing STD in the right basal area and endocardial pacing in the left. Ventricular junction stimuli exhibited symmetrical STD patterns, low near the stimulus but increasing sharply towards the apex, peaking on the left in the apical region. Furthermore, variability in the imbrication or helix angle did not impact the activation sequences. We conclude that in many relevant modelling contexts, the variability in myocardial fibre orientation can play an important role in the resulting activation sequences and should be accounted for. image Key points: The primary fibre orientation has modest impact on activation duration and location of most discrete activation features for ectopic stimuli, but introduces variability in activation sequence. For free wall stimuli, the standard deviation (STD) was highest on the stimulated surface, indicating that deviations are largest in early activation and diminish as activation reaches remote heart regions. For apical stimuli, activation patterns were insensitive to fibre orientation variations, but the STD had complementary maxima, strongly dependent on pacing surface. Epicardial pacing produced largest STD in right basal area while endocardial pacing affected left basal area, indicating strong dependence on fibre structure. For stimuli at both anterior and posterior ventricular junctions, STD patterns were symmetrical, with low values near stimulus and sharp increases towards apex, peaking in left apical region. The helical fibre orientation showed no relevant fluctuations in activation sequence.

Cardiac digital twins (CDTs) are emerging as powerful tools in personalized medicine, providing subject-specific models to simulate and understand cardiac function. A central challenge in constructing CDTs is accurately personalizing the structure and function of the His-Purkinje system (HPS), which determines ventricular activation. In this study, we leveraged a novel modified Geodesic-BP method to infer early activation sites (EASs) from epicardial activation times. The EASs can then serve as a surrogate for Purkinje-myocardial junctions, facilitating anterograde ventricular activation. We used both experimental porcine (N = 5) and synthetic (N = 5) datasets of epicardial activation times measured or assigned to locations on an electrode sock. For both datasets, we optimized for initial estimates of 5, 50, 100, and 200 EASs and assessed output variability by repeating the inference process 10 times. Assessments were based on matching the predicted activation times at both the epicardial locations and from intracardiac measurements made with multielectrode needles, and throughout the ventricular myocardium for the synthetic dataset. The algorithm could consistently recover global ventricular activation patterns from epicardial data alone. For the experimental dataset, the minimum and maximum mean absolute differences were 0.19 ms and 3.86 ms on the epicardial sock and 2.65 ms and 10.69 ms for the needles. For the synthetic dataset, the corresponding values were 0.13 ms and 2.81 ms on the sock and 2.42 ms and 14.07 ms ms throughout the ventricular myocardium. However, discrepancies between the epicardial surface and intramural myocardium, overfitting of EASs, and variability across repeated runs revealed key limitations. These findings highlight both the overall potential and current limitations of inferring EASs using the proposed optimization approach. They demonstrate the feasibility of deriving informative activation patterns from limited data, while underscoring the need to incorporate stronger physiological priors and anatomical constraints. Ultimately, our results motivate future efforts to refine simulation-based personalization frameworks, improve robustness, and enhance the physiological realism of CDTs for more accurate and reliable applications.

Torsade de Pointes (TdP) is a type of ventricular tachycardia that can occur as a side effect of several medications. The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a novel testing paradigm that utilizes single cell electrophysiological simulations to predict TdP risk for drugs that could potentially be used clinically. However, the effects on mechanical performance and mechano-electrical feedback are neglected. Here, we demonstrate that including electromechanical simulations in CiPA testing can provide additional insights into the predicted drug-induced TdP risk. In this work, we analyzed six drugs, namely flecainide, ibutilide, metronidazole, mexiletine, quinidine and ranolazine. We compared previously classified risks (low, intermediate, high) with our fully coupled electromechanical simulation results based upon the action potential, the electromechanical window, and the maximum active tension [1]. For ranolazine and metronidazole the predicted risk changed from low to intermediate and intermediate to high, respectively. For the latter, while electrophysiological markers indicated a low risk, the active tension decreased by 58% which can result in a loss of heart function. Therefore, adding mechanics to CiPA testing results in an altered prediction of drug-related TdP risk.

Ventricular arrhythmia frequently complicates myocardial ischemic events, sometimes to devastating ends. Accurate arrhythmia prediction in this setting could improve outcomes, yet traditional models struggle with the temporal complexity of the data. This study employs a Long Short-Term Memory (LSTM) network to predict the time to the next premature ventricular contraction (PVC) using high-resolution experimental data. We analyzed electrograms from 11 large animal experiments, identifying 1832 PVCs, and computed time-to-PVC. An LSTM model (247 inputs, 1024 hidden units) was trained on 10 experiments, with one held out for testing, achieving a validation MAE of 8.6 seconds and a test MAE of 135 seconds (loss 68.5). Scatter plots showed strong validation correlation and a positive test trend, suggesting the potential of this approach.