ArticleFrontiers in physiology2022
Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction.NPJ digital medicine · 2025Article
- Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.BMC medical informatics and decision making · 2024Article
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10 authors at 2 institutions in 1 country.
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Abstract
Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia. In this study, an automatic system combining denoising and segmentation modules was developed to detect the deviation of the ST-segment and J point. We proposed a ECG Bidirectional Transformer network that applied in both denoising and segmentation tasks. The denoising model achieved RMSE
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