Accessory pathway (AP) localization from the 12-lead ECG remains clinically relevant in pediatric Wolff–Parkinson–White (WPW) syndrome, as it can guide pre-procedural planning, catheter access, and identification of high-risk septal locations. However, accurate localization in children remains challenging due to age-dependent variability and the limited reliability of existing algorithms, which are often developed in adult populations and rely on subjective feature interpretation.
We propose an interpretable, simulation-driven framework for AP laterality classification based on decision trees trained on automatically extracted ECG features. A patient-specific cardiac digital twin was constructed from CT imaging, including ventricular geometry, fiber orientation, and a cardiac conduction system network. A dataset of 696 simulated ECGs was generated across 174 AP locations and four pre-excitation timings. Features capturing early activation were extracted from all leads, and ground-truth labels were defined directly from the simulations, enabling objective left-versus-right classification.
The model achieved high performance in simulated data (balanced accuracy 0.968, macro-F1 0.956), with only 18 misclassifications. The resulting decision tree was compact and interpretable, relying on a small number of features, primarily QRS balance in lead V1, complemented by V2 short-window slope and lead III QRS balance. When evaluated on an independent clinical dataset of 10 pediatric ECGs, the model achieved a balanced accuracy of 0.708 and macro-F1 of 0.697, indicating preliminary generalization to clinical ECGs.
These results show that ventricular laterality can be captured using a limited set of quantitative ECG descriptors derived from simulated data. The proposed framework provides interpretable decision rules linked to electrophysiological mechanisms and offers a reproducible alternative to subjective ECG assessment, representing a step toward integrating cardiac digital twins and machine learning for non-invasive pre-procedural planning in pediatric WPW.