ArticleCardiovascular digital health journal2023
Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence.
Article in Cardiovascular digital health journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Short-term Metformin Protects Against Glucocorticoid-Induced Toxicity in Healthy Individuals: A Randomized, Double-Blind, Placebo-Controlled Trial.Diabetes care · 2025 · on this mapTrial
- R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Discrimination of atrial fibrillation burden using cardiac magnetic resonance imaging.Heart rhythm O2 · 2026Article
- A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study.JMIR mHealth and uHealth · 2026Observational
- Review
- Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.Scientific reports · 2025Article
- Effect of Ticagrelor Versus Clopidogrel After Off-Pump Coronary Artery Bypass Grafting on Postoperative Atrial Fibrillation: A Cohort Study.Journal of the American Heart Association · 2024Article
- The diagnostic efficiency of artificial intelligence based 2 hours Holter monitoring in premature ventricular and supraventricular contractions detection.Clinical cardiology · 2024Article
- Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm.Cardiovascular digital health journal · 2024Article
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Authors and funding
13 authors.
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Abstract
Background: Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. However, AF burden is not routinely measured in clinical practice. An artificial intelligence (AI)-based tool could facilitate the assessment of AF burden. Objective: We aimed to compare the assessment of AF burden performed manually by physicians with that measured by an AI-based tool. Methods: We analyzed 7-day Holter electrocardiogram (ECG) recordings of AF patients included in the prospective, multicenter Swiss-AF Burden cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool (Cardiomatics, Cracow, Poland). We evaluated the agreement between both techniques by means of Pearson correlation coefficient, linear regression model, and Bland-Altman plot. Results: We assessed the AF burden in 100 Holter ECG recordings of 82 patients. We identified 53 Holter ECGs with 0% or 100% AF burden, where we found a 100% correlation. For the remaining 47 Holter ECGs with an AF burden between 0.01% and 81.53%, Pearson correlation coefficient was 0.998. The calibration intercept was -0.001 (95% CI -0.008; 0.006), and the calibration slope was 0.975 (95% CI 0.954; 0.995; multiple R Conclusion: The assessment of AF burden with an AI-based tool provided very similar results compared to manual assessment. An AI-based tool may therefore be an accurate and efficient option for the assessment of AF burden.
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