Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals

• URL: http://arxiv.org/abs/2511.05973v1
• Date: Sat, 08 Nov 2025 11:39:59 GMT
• Title: Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals
• Authors: Alice Ragonesi, Stefania Fresca, Karli Gillette, Stefan Kurath-Koller, Gernot Plank, Elena Zappon,
• Abstract summary: We present a Deep Learning (DL) model for the localization of single manifest APs across 24 cardiac regions.<n>Our model achieves localization accuracy above 95%, with a sensitivity of 94.32% and specificity of 99.78%.<n>This work demonstrates the potential of combining cardiac digital twins with explainable DL to enable accurate, transparent, and non-invasive AP localization.
• Score: 0.5473229173811306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wolff-Parkinson-White (WPW) syndrome is a cardiac electrophysiology (EP) disorder caused by the presence of an accessory pathway (AP) that bypasses the atrioventricular node, faster ventricular activation rate, and provides a substrate for atrio-ventricular reentrant tachycardia (AVRT). Accurate localization of the AP is critical for planning and guiding catheter ablation procedures. While traditional diagnostic tree (DT) methods and more recent machine learning (ML) approaches have been proposed to predict AP location from surface electrocardiogram (ECG), they are often constrained by limited anatomical localization resolution, poor interpretability, and the use of small clinical datasets. In this study, we present a Deep Learning (DL) model for the localization of single manifest APs across 24 cardiac regions, trained on a large, physiologically realistic database of synthetic ECGs generated using a personalized virtual heart model. We also integrate eXplainable Artificial Intelligence (XAI) methods, Guided Backpropagation, Grad-CAM, and Guided Grad-CAM, into the pipeline. This enables interpretation of DL decision-making and addresses one of the main barriers to clinical adoption: lack of transparency in ML predictions. Our model achieves localization accuracy above 95%, with a sensitivity of 94.32% and specificity of 99.78%. XAI outputs are physiologically validated against known depolarization patterns, and a novel index is introduced to identify the most informative ECG leads for AP localization. Results highlight lead V2 as the most critical, followed by aVF, V1, and aVL. This work demonstrates the potential of combining cardiac digital twins with explainable DL to enable accurate, transparent, and non-invasive AP localization.

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