ReviewInternational journal of environmental research and public health2020
A Review of Atrial Fibrillation Detection Methods as a Service.
Review in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.Current cardiology reviews · 2025Pooled it
- Music-Inspired MK545Pat for AF/PAF/Non-AF Classification Using ECG Signals.Bioengineering (Basel, Switzerland) · 2026Article
- RT-AFNet: A Hybrid ResNet-Transformer Architecture with Multi-Scale Fusion for Atrial Fibrillation Detection.Biosensors · 2026Article
- Atrial Fibrillation Detection on the Embedded Edge: Energy-Efficient Inference on a Low-Power Microcontroller.Sensors (Basel, Switzerland) · 2025Article
- Transformer-based heart language model with electrocardiogram annotations.Scientific reports · 2025Article
- Cryoablation and radiofrequency ablation during mitral valve surgery for rheumatic mitral valve disease: a retrospective cohort study.Frontiers in cardiovascular medicine · 2025Article
- Insights into the Role of Galectin-3 as a Diagnostic and Prognostic Biomarker of Atrial Fibrillation.Disease markers · 2023Review
- Wearable Devices Combined with Artificial Intelligence-A Future Technology for Atrial Fibrillation Detection?Sensors (Basel, Switzerland) · 2022Article
- Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review.Sensors (Basel, Switzerland) · 2022Review
- ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patientsEuropean heart journal. Digital health · 2022Review
- Accurate detection of atrial fibrillation events with R-R intervals from ECG signals.PloS one · 2022Article
- CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset.Frontiers in cardiovascular medicine · 2022Article
- Review of Deep Learning-Based Atrial Fibrillation Detection Studies.International journal of environmental research and public health · 2021Review
- AFibNet: an implementation of atrial fibrillation detection with convolutional neural network.BMC medical informatics and decision making · 2021Article
- Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention.International journal of environmental research and public health · 2021Article
- A Smart Service Platform for Cost Efficient Cardiac Health Monitoring.International journal of environmental research and public health · 2020Review
- Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings.BioMed research international · 2020Article
- Signal-quality-aware multisensor fusion for atrial fibrillation detection.Healthcare technology lettersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.
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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.