ArticlePatterns (New York, N.Y.)2024
Early warning of atrial fibrillation using deep learning.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- From Chaos to Care: Personalized AI for Early Cardiac Arrhythmia Warning.Machine learning with applications · 2026Article
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Artificial intelligence for atrial fibrillation prediction while in sinus rhythm: a narrative review.European heart journal. Digital health · 2026Review
- Early differentiation between paroxysmal and persistent atrial fibrillation based on interpretable machine learning: a multicenter retrospective study.BioData mining · 2026Article
- Machine Learning and Arrhythmia: Advances in Atrial Fibrillation Detection and Management.Current atherosclerosis reports · 2025Review
- AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review.JMIR mHealth and uHealth · 2025Article
- The predictive power of left atrial reservoir strain for long-term atrial fibrillation in the Atherosclerosis Risk in Communities study.Heart rhythm · 2025Article
- DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information.Patterns (New York, N.Y.) · 2025Article
- Explainable artificial intelligence for stroke risk stratification in atrial fibrillation.European heart journal. Digital health · 2025Review
- Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care.Biomedicines · 2025Review
- The Era of Preemptive Medicine: Developing Medical Digital Twins through Omics, IoT, and AI Integration.JMA journal · 2025Review
- Machine learning in the prevention of heart failure.Heart failure reviews · 2025Review
- Early warning of atrial fibrillation using deep learning.Patterns (New York, N.Y.) · 2024Article
- Consider this a WARNing.Patterns (New York, N.Y.) · 2024Article
- Towards automatic home-based sleep apnea estimation using deep learning.NPJ digital medicine · 2024Article
- A two-stage deep learning framework for predicting the onset of Atrial fibrillation using RR interval-based embeddings.Biocybernetics and biomedical engineeringArticle
Corrections and comments
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R-to-R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.
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Registered trials
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