ArticleEuropean heart journal. Digital health2026
Explainable electrocardiogram interpretation using deep learning-based semantic segmentation.
Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Aims: Accurate electrocardiogram (ECG) waveform delineation, rhythm classification, and median beat generation are interdependent steps whose joint modelling improves consistency for downstream computerized diagnostic tasks. This study aimed to develop a lead-agnostic segmentation model that performs these tasks by segmenting individual leads and aggregating predictions in post-processing. Methods and results: A DeepLabV3-based neural network was trained to segment ECG leads into 20 waveform and rhythm classes using 1931 annotated ECGs and 33 093 ECGs with physician-verified diagnostic statements. Post-processing combined lead-wise predictions to delineate intervals, classify rhythm, and construct median beats. Performance was evaluated on internal ( Conclusion: This study demonstrates a robust, clinically applicable, vendor- and lead-agnostic deep learning model for ECG analysis, encompassing waveform delineation, rhythm classification, and median beat construction.
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