ArticlePloS one2020
Machine learning detection of Atrial Fibrillation using wearable technology.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2021Pooled it
- R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Promoting Rehabilitation Using a Multimodal Internet of Things-Based Patient Monitoring System in a Smart Hospital.IEEE journal of translational engineering in health and medicine · 2026Article
- Real-Time Heartbeat Classification on Distributed Edge Devices: A Performance and Resource Utilization Study.Sensors (Basel, Switzerland) · 2025Article
- Biomarkers for Personalised Primary or Secondary Prevention in Cardiovascular Diseases: A Rapid Scoping Review.International journal of molecular sciences · 2025Article
- Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.BMC primary care · 2024Article
- Improving Automatic Smartwatch Electrocardiogram Diagnosis of Atrial Fibrillation by Identifying Regularity within Irregularity.Sensors (Basel, Switzerland) · 2023Article
- Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.Sensors (Basel, Switzerland) · 2023Review
- A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection.Sensors (Basel, Switzerland) · 2023Article
- Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network.Frontiers in physiology · 2023Article
- Wearable activity trackers-advanced technology or advanced marketing?European journal of applied physiology · 2022Review
- Is machine learning the future for atrial fibrillation screening?Cardiovascular digital health journal · 2022Review
- Wearable Devices for Physical Monitoring of Heart: A Review.Biosensors · 2022Review
- Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation.Diagnostics (Basel, Switzerland) · 2022Article
- Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study.Diagnostics (Basel, Switzerland) · 2022Review
- Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects.Sensors (Basel, Switzerland) · 2022Review
- Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View.JMIR medical informatics · 2022Review
- Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease.Frontiers in cardiovascular medicine · 2022Article
- Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register-based medical hospital data and neural networks.European heart journal. Digital health · 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
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundAtrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stroke by approximately two-thirds and can provide a 10% reduction in overall mortality. There has been increased interest in detecting AF due to its increased incidence and the possibility to prevent AF-related strokes. Inexpensive consumer devices which measure the ECG may have the potential to accurately detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF.
methodsWe designed a novel AF detection algorithm using a de-correlated Lorenz plot of 60 consecutive RR intervals. In order to reduce the volume of data, the resulting images were compressed using a wavelet transformation (JPEG200 algorithm) and the compressed images were used as input data to a Support Vector Machine (SVM) classifier. We used the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) Atrial Fibrillation database and the MIT-BIH Arrhythmia database as training data and verified the algorithm performance using RR intervals collected using an inexpensive consumer heart rate monitor device (Polar-H7) in a case-control study.
resultsThe SVM algorithm yielded excellent discrimination in the training data with a sensitivity of 99.2% and a specificity of 99.5% for AF. In the validation data, the SVM algorithm correctly identified AF in 79/79 cases; sensitivity 100% (95% CI 95.4%-100%) and non-AF in 328/336 cases; specificity 97.6% (95% CI 95.4%-99.0%).
conclusionsAn inexpensive wearable heart rate monitor and machine learning algorithm can be used to detect AF with very high accuracy and has the capability to transmit ECG data which could be used to confirm AF. It could potentially be used for intermittent screening or continuously for prolonged periods to detect paroxysmal AF. Further work could lead to cost-effective and accurate estimation of AF burden and improved risk stratification in AF.
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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.