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:

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:

Atrial shape has been linked to atrial fibrillation (AF), yet many aspects remain poorly understood. Recent evidence suggests that the right atrium (RA) affects AF vulnerability in both chambers, highlighting the need for bi-atrial analysis. However, open bi-atrial cohorts are scarce. Statistical shape models (SSMs) address this gap, but model variability only linearly and require surface correspondence. Recently, deep learning (DL)-based generative models have emerged as an alternative. We present a DL-based bi-atrial generative model, extending an existing signed distance function based auto-decoder framework for the left atrium (LA) to a dual-head network with shared trunk, trained on 344 patients from a public CT dataset. We investigated latent dimensionality effects on reconstruction and generation performance. The model produced anatomically realistic geometries with separated atria, variable shapes, and diverse LA appendage morphologies. Volumes and volume ratios aligned with clinically observed ranges. Reconstruction and generation improved with latent dimension, with dimension 64 best balancing complexity and quality where mean Chamfer distances were 1.2 mm for reconstruction and 2.5 mm (LA) and 2.2 mm (RA) for generation. LA reconstruction distances exceeded those in the RA, likely due to greater topological complexity and the comparatively lower RA data fidelity. Compared to the existing bi-atrial SSM, the DL model required higher dimension, possibly confounded by dataset differences. Overall, loss plateaued within 400 epochs for all models, suggesting early stopping for future work. In conclusion, the model can support virtual cohort generation for AF in silico studies; allowing conditioning on clinical metadata in future work.