ArticleEuropean heart journal. Digital health2022
Short-term prediction of atrial fibrillation from ambulatory monitoring ECG using a deep neural network.
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Artificial intelligence capabilities in identifying atrial fibrillation using baseline sinus rhythm ECG : a systematic review.Open heart · 2025Pooled it
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- Artificial Intelligence for Prediction and Detection of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms and Ambulatory Monitoring.Biomedicines · 2026Review
- Artificial intelligence for atrial fibrillation prediction while in sinus rhythm: a narrative review.European heart journal. Digital health · 2026Review
- Near-term prediction of sustained ventricular arrhythmias applying artificial intelligence to single-lead ambulatory electrocardiogram.European heart journal · 2025Article
- Atrial Fibrillation Prediction Based on Recurrence Plot and ResNet.Sensors (Basel, Switzerland) · 2024Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- An artificial intelligence-enabled Holter algorithm to identify patients with ventricular tachycardia by analysing their electrocardiogram during sinus rhythm.European heart journal. Digital health · 2024Article
- Early warning of atrial fibrillation using deep learning.Patterns (New York, N.Y.) · 2024Article
- Adopting artificial intelligence in cardiovascular medicine: a scoping review.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias.NPJ digital medicine · 2023Article
- Can 24 h of ambulatory ECG be used to triage patients to extended monitoring?Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Article
- Automatic algorithmic driven monitoring of atrioventricular nodal re-entrant tachycardia ablation to improve procedural safety.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Atrial fibrillation (AF) is associated with significant morbidity but remains underdiagnosed. A 24 h ambulatory electrocardiogram (ECG) is largely used as a tool to document AF but yield remains limited. We hypothesize that a deep learning model can identify patients at risk of AF in the 2 weeks following a 24 h ambulatory ECG with no documented AF. Methods and results: We identified a training set of Holter recordings of 7-15 days duration, in which no AF could be found in the first 24 h. We trained a neural network to predict the presence or absence of AF in the 15 following days, using only the first 24 h of the recording. We evaluated the neural network on a testing set and an external data set not used during algorithm development. In the testing data set, out of 9993 Holters with no AF on the first day, we found 361 (4%) recordings with AF within the 15 subsequent days of monitoring [5808, 218 (4%), respectively in the external data set]. The neural network could discriminate future AF with an area under the receiver operating curve, a sensitivity, and specificity of 79.4%, 76%, and 69%, respectively (75.8%, 78%, and 58% in the external data set), and outperformed ECG features previously shown to be predictive of AF. Conclusion: We show here the very first study of short-term AF prediction using 24 h Holter monitoring. This could help identify patients who would benefit the most from longer recordings and proactively initiate treatment and AF mitigation strategies in high-risk patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.