Sudden cardiac death risk is 2-3-fold higher in athletes than in non-athletes. We classify sports-related cardiac arrhythmias using a novel explainability framework comprising data analysis, model interpretability, post-hoc visualisation, and systematic assessment. Two neural networks—one with interpretable sinc convolution and one with standard convolution—were trained on general-population ECGs (PhysioNet, n=88,253, 30 arrhythmias, three continents) and tested on professional footballers (PF12RED, n=161) via domain adaptation for normal sinus rhythm (NSR), sinus bradycardia (SB), incomplete right bundle branch block (IRBBB), and T-wave inversion (TWI).

Sinc convolution achieved superior NSR detection (AUROC 0.75 vs 0.70), whilst standard convolution excelled at SB (0.74 vs 0.73), IRBBB (0.66 vs 0.58), and TWI (0.59 vs 0.54). Gradient-weighted Class Activation Mapping revealed that sinc models focus on physiologically relevant ECG segments (the PR interval for NSR/SB and the T wave for TWI). We hypothesise that sinc convolution better captures periodic rhythms but struggles with complex morphological patterns, suggesting architectural choice should align with underlying cardiac pathophysiology.

Graphical abstract Abbreviations: AI, artificial intelligence; AUPRC, area under the precision-recall curve; AUROC, area under the receiver operating characteristic curve; Conv, convolution; ECG, electrocardiogram; Grad-CAM, gradient-weighted class activation mapping; IAVB, first-degree atrioventricular block; IRBBB, incomplete right bundle branch block; LAD, left axis deviation; LBBB, left bundle branch block; LVH, left ventricular hypertrophy; NSR, normal sinus rhythm; QT, QT interval; RAD, right axis deviation; RBBB, right bundle branch block; RVH, right ventricular hypertrophy; SA, sinus arrhythmia; SB, sinus bradycardia; TWI, T-wave inversion; xAI, explainable artificial intelligence.

The authors have declared no competing interest.

EV, LH, and MV are funded by The Podium Institute of Sports Medicine and Technology. AB is supported by the Royal Society University Research Fellowship (Grant No. URFR1'314). PL acknowledges funding from the Oxford NIHR Biomedical Research Centre, Medical Research Council and British Heart Foundation. MH is funded by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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The data is available from the PhysioNet site and GitHub repository for the PF12RED data: The PhysioNet Challenge 21 and PF12RED datasets are open-source datasets and can be downloaded from https://physionet.org/content/challenge-2021/1.0.3/#files and https://github.com/dradolfomunoz/PF12RED respectively.

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Funding information EV, LH, and MV are funded by The Podium Institute of Sports Medicine and Technology. AB is supported by the Royal Society University Research Fellowship (Grant No. URFR1’314). PL acknowledges funding from the Oxford NIHR Biomedical Research Centre, Medical Research Council and British Heart Foundation. MH is funded by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.