ArticleSensors (Basel, Switzerland)2020
Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Identification of atrial fibrillation using heart rate variability: a meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.JMIR cardio · 2026Article
- Clinically interpretable multiclass neural network for discriminating cardiac diseases.Heliyon · 2025Article
- Wearable ECG Device and Machine Learning for Heart Monitoring.Sensors (Basel, Switzerland) · 2024Article
- An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.Heliyon · 2024Article
- Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller.Sensors (Basel, Switzerland) · 2023Article
- A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG.Diagnostics (Basel, Switzerland) · 2023Article
- Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search.Scientific reports · 2023Article
- Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review.JMIR cardio · 2023Article
- Importance of Electrode Selection and Number in Reconstructing Standard Twelve Lead Electrocardiograms.Biomedicines · 2023Article
- Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Wearable and Portable Devices for Acquisition of Cardiac Signals while Practicing Sport: A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Assessment of electrical dyssynchrony in cardiac resynchronization therapy: 12-lead electrocardiogram vs. 96-lead body surface map.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023Article
- An ensemble of features based deep learning neural network for reduction of inappropriate atrial fibrillation detection in implantable cardiac monitors.Heart rhythm O2 · 2023Article
- Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection.Journal of clinical medicine · 2022Article
- DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification.Sensors (Basel, Switzerland) · 2022Article
- Review of Deep Learning-Based Atrial Fibrillation Detection Studies.International journal of environmental research and public health · 2021Review
- Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set.Sensors (Basel, Switzerland) · 2021Article
- How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.Cardiovascular research · 2021Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the "AF Classification from a Short Single Lead ECG Recording" database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1-93.0%), 90.2% (CI: 86.2-94.3%) and 90.8% (CI: 88.1-93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.
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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.