ArticleScientific reports2024
Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Artificial Intelligence for Prediction and Detection of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms and Ambulatory Monitoring.Biomedicines · 2026Review
- Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness.Frontiers in cardiovascular medicine · 2026Review
- Privacy-preserving adaptive arrhythmia monitoring with wearable ECG, smartphone PPG, and federated on-device learning.Frontiers in digital health · 2026Article
- AI-Powered Precision: Revolutionizing Atrial Fibrillation Detection with Electrocardiograms.Journal of clinical medicine · 2025Review
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals.
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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.