Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation
A new framework called CardioMix has been introduced for ECG segmentation, addressing the challenge of limited annotated data. This framework utilizes a bidirectional CutMix strategy guided by cardiac patterns to enhance both labeled and unlabeled data. Extensive experiments show that CardioMix outperforms existing methods in ECG delineation across various datasets.
- ▪Accurate ECG segmentation is crucial for cardiovascular diagnostics but is hindered by a lack of annotated data.
- ▪CardioMix enriches labeled data with variations from unlabeled data while maintaining physiological relevance.
- ▪The framework is compatible with various semi-supervised segmentation algorithms and consistently outperforms existing strategies.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15722 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Machine Learning arXiv:2605.15722 (cs) [Submitted on 15 May 2026] Title:Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation Authors:Jeonghwa Lim, Minje Park, Sunghoon Joo View a PDF of the paper titled Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation, by Jeonghwa Lim and 2 other authors View PDF HTML (experimental) Abstract:Accurate delineation of electrocardiogram (ECG), the segmentation of meaningful waveform features, is crucial for cardiovascular diagnostics. However, the scarcity of annotated data poses a significant challenge for training deep learning models.
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