For the heart to pump blood throughout the body, electrical impulses that trigger the cellular contraction must be generated and spread through the myocardial tissue. These signals propagate faster along the longitudinal cardiac fiber direction than the transverse direction, conferring the heart with anisotropic conduction properties. Therefore, the arrangement of the fibers within the tissue governs the impulse propagation. Given the variability of the fiber direction across the heart and between patients, incorporating it into electrophysiological models would enhance our understanding of the mechanisms and progression of different heart conditions, such as atrial fibrillation (AF). The study of this common cardiac arrhythmia relies on analyzing electrical recordings of the heart, known as electrograms (EGMs), which, if integrated with the patient’s fiber architecture into cardiac models, can enable effective personalized treatment. Over the years, researchers have proposed different approaches to estimate the fiber direction from EGMs. However, these methods have been evaluated in different, usually simplistic, cardiac tissue models, making their comparison, and therefore selection of the most accurate approach for clinical and research applications, challenging.

The current study aims to identify the best fiber direction estimation method under consistent and realistic conditions. To achieve this goal, synthetic EGMs and local activation time (LAT) maps were generated from 2D and 3D monodomain models that mimicked the muscle bundle, atrial bilayer, and ventricular transmural fiber rotation structures. A comparison analysis of existing fiber direction estimation methods, first as described by their authors and then standardized to have the same spatial resolution, showed the superior performance of the techniques based on fitting an ellipse to local conduction velocity or conduction slowness vectors from a whole LAT map. The estimation accuracy of these methods can be further improved by increasing the number of vectors to which the ellipse is fitted. Nonetheless, given the influence of underlying layers in the epicardial recordings, the estimation error increases in the tissue models where fibers in the epicardial and endocardial layers run perpendicularly. The effect on the estimate of such architecture, characteristic of the inferior side of the right atria and the ventricles, can be accounted for by combining epicardial electrical recordings obtained after pacing either in the endocardium or the epicardium. Although a preliminary assessment of the estimation methods was carried out with human EGMs, future studies should focus on validating the methods in a controlled experimental framework and refining them for more localized fiber direction estimation. All in all, the automation of the techniques and their integration into electrophysiological models brings us a step closer to creating valuable clinical tools for diagnosing and treating electropathologies.