ReviewInternational journal of environmental research and public health2021
Review of Deep Learning-Based Atrial Fibrillation Detection Studies.
Review in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2024Pooled it
- Risk Factors for Cardiovascular Disease: Epidemiology, Screening, Prevention, and Therapeutic Interventions.MedComm · 2026Review
- KAN-Former: a lightweight ECG model for real-time atrial fibrillation detection on wearable devices.Frontiers in bioengineering and biotechnology · 2026Article
- Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Integration of EHR and ECG Data for Predicting Paroxysmal Atrial Fibrillation in Stroke Patients.Bioengineering (Basel, Switzerland) · 2025Article
- Atrial Fibrillation and Atrial Flutter Detection Using Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.PloS one · 2025Article
- Harnessing artificial intelligence of things for cardiac sensing: current advances and network-based perspectives.Frontiers in public health · 2025Article
- Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.BMC primary care · 2024Article
- Higher-Order Spectral Analysis Combined with a Convolution Neural Network for Atrial Fibrillation Detection-Preliminary Study.Sensors (Basel, Switzerland) · 2024Article
- Edge AI Model Deployed for Real-Time Detection of Atrial Fibrillation Risk during Sinus Rhythm.Journal of clinical medicine · 2024Article
- Article
- Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives.Frontiers in cardiovascular medicine · 2024Review
- Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet.Frontiers in physiology · 2024Article
- Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation.Reviews in cardiovascular medicine · 2023Review
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
- Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Editorial: Exploring mechanisms of cardiac rhythm disturbances using novel computational methods: Prediction, classification, and therapy.Frontiers in physiology · 2023Article
- CoAt-Mixer: Self-attention deep learning framework for left ventricular hypertrophy using electrocardiography.PloS one · 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
9 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.
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.