ArticleScientific reports2025
Self-supervised VICReg pre-training for Brugada ECG detection.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Electrophysiological Modeling and Molecular Dynamics Simulations of Cardiac Sodium Channels in Brugada Syndrome.BioMed research international · 2026Article
- Deep representation learning using layer-wise VICReg losses.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Existing deep learning algorithms for electrocardiogram (ECG) classification rely on supervised training approaches requiring large volumes of reliably labeled data. This limits their applicability to rare cardiac diseases like Brugada syndrome (BrS), often lacking accurately labeled ECG examples. To address labeled data constraints and the resulting limitations of supervised training approaches, we developed a novel deep learning model for BrS ECG classification using the Variance-Invariance-Covariance Regularization (VICReg) architecture for self-supervised pre-training. The VICReg model outperformed a state-of-the-art neural network in all calculated metrics, achieving an area under the receiver operating and precision-recall curves of 0.88 and 0.82, respectively. We used the VICReg model to identify missed BrS cases and hence refine the previously underestimated institutional BrS prevalence and patient outcomes. Our results provide a novel approach to rare cardiac disease identification and challenge existing BrS prevalence estimates offering a framework for other rare cardiac conditions.
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Registered trials
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