ArticlePloS one2024
Deep learning based ECG segmentation for delineation of diverse arrhythmias.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multi-Tier Validation of a Single-Lead Wearable ECG Signal-Processing Pipeline: Clinical Comparison and Algorithmic Benchmarking on Public Expert-Annotated Databases.Sensors (Basel, Switzerland) · 2026Article
- Explainable electrocardiogram interpretation using deep learning-based semantic segmentation.European heart journal. Digital health · 2026Article
- AI-Based QRS Onset Detection in the Early Ventricular Activation Site ECGs.IEEE journal of biomedical and health informatics · 2026Article
- Interpretable manifold learning for T-wave alternans assessment with electrocardiographic imaging.Engineering applications of artificial intelligence · 2025Article
- Deep learning-based beat-to-beat delineation of heart sounds and fiducial points in seismocardiography.Frontiers in digital health · 2025Article
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Authors and funding
12 authors.
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Abstract
Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy.
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