ArticleIntensive care medicine experimental2023
A deep learning model for the classification of atrial fibrillation in critically ill patients.
Article in Intensive care medicine experimental, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- A systematic review on the influence of coagulopathy and immune activation on New Onset Atrial Fibrillation in patients with sepsis.PloS one · 2025Pooled it
- Leveraging ECG foundation models in critical care for sinus rhythm and atrial fibrillation classification.Intensive care medicine experimental · 2026Article
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Interpretable machine learning to predict NOAF in ICU patients with CKD: validation in US and Chinese cohorts.Frontiers in medicine · 2026Article
- [A lightweight classification network for single-lead atrial fibrillation based on depthwise separable convolution and attention mechanism].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Prevalence, Risk Factors, and Mortality of New-Onset Atrial Fibrillation in Mechanically Ventilated Critically Ill Patients.Journal of clinical medicine · 2024Article
- Research priorities for the study of atrial fibrillation during acute and critical illness: recommendations from the Symposium on Atrial Fibrillation in Acute and Critical Care.BMC proceedings · 2024Article
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7 authors.
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Abstract
backgroundAtrial fibrillation (AF) is the most common cardiac arrhythmia in the intensive care unit and is associated with increased morbidity and mortality. New-onset atrial fibrillation (NOAF) is often initially paroxysmal and fleeting, making it difficult to diagnose, and therefore difficult to understand the true burden of disease. Automated algorithms to detect AF in the ICU have been advocated as a means to better quantify its true burden.
resultsWe used a publicly available 12-lead ECG dataset to train a deep learning model for the classification of AF. We then conducted an external independent validation of the model using continuous telemetry data from 984 critically ill patients collected in our institutional database. Performance metrics were stratified by signal quality, classified as either clean or noisy. The deep learning model was able to classify AF with an overall sensitivity of 84%, specificity of 89%, positive predictive value (PPV) of 55%, and negative predictive value of 97%. Performance was improved in clean data as compared to noisy data, most notably with respect to PPV and specificity.
conclusionsThis model demonstrates that computational detection of AF is currently feasible and effective. This approach stands to improve the efficiency of retrospective and prospective research into AF in the ICU by automating AF detection, and enabling precise quantification of overall AF burden.
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