ArticleHeart rhythm O22020
Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application.
Article in Heart rhythm O2, 2020. 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 26 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
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.Journal of personalized medicine · 2026Review
- Multiclass Arrhythmia Classification Using Multimodal Smartwatch Photoplethysmography Signals Collected in Real-Life Settings.IEEE transactions on bio-medical engineering · 2026Article
- Atrial Fibrillation Detection from At-Rest PPG Signals Using an SDOF-TF Method.Sensors (Basel, Switzerland) · 2026Article
- Motion Artifacts Removal from Measured Arterial Pulse Signals at Rest: A Generalized SDOF-Model-Based Time-Frequency Method.Sensors (Basel, Switzerland) · 2025Article
- Wearable Technology in Cardiology: Advancements, Applications, and Future Prospects.Reviews in cardiovascular medicine · 2025Review
- Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation.Biophysics reviews · 2025Review
- Leveraging AI-enhanced digital health with consumer devices for scalable cardiovascular screening, prediction, and monitoring.NPJ cardiovascular health · 2025Review
- Atrial Fibrillation Prediction Based on Recurrence Plot and ResNet.Sensors (Basel, Switzerland) · 2024Article
- Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms.IEEE journal of biomedical and health informatics · 2024Article
- Photoplethysmography based atrial fibrillation detection: a continually growing field.Physiological measurement · 2024Review
- New Approach to Equitable Intervention Planning to Improve Engagement and Outcomes in a Digital Health Program: Simulation Study.JMIR diabetes · 2024Article
- Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives.Frontiers in cardiovascular medicine · 2024Review
- Review
- The 2023 wearable photoplethysmography roadmap.Physiological measurement · 2023Review
- A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG.Diagnostics (Basel, Switzerland) · 2023Article
- Beat-to-beat finger photoplethysmography in atrial fibrillation patients undergoing electrical cardioversion.Scientific reports · 2023Article
- Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting.Frontiers in cardiovascular medicine · 2023Article
- Federated Learning via Augmented Knowledge Distillation for Heterogenous Deep Human Activity Recognition Systems.Sensors (Basel, Switzerland) · 2022Article
- Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study.JMIR formative research · 2022Article
Corrections and comments
- Erratum issuedErratum.2021
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAtrial fibrillation (AF), a common cause of stroke, often is asymptomatic. Smartphones and smartwatches can detect AF using heart rate patterns inferred using photoplethysmography (PPG); however, enhanced accuracy is required to reduce false positives in screening populations.
objectiveThe purpose of this study was to test the hypothesis that a deep learning algorithm given raw, smartwatch-derived PPG waveforms would discriminate AF from normal sinus rhythm better than algorithms using heart rate alone.
methodsPatients presenting for cardioversion of AF (n = 51) were given wrist-worn fitness trackers containing PPG sensors (Jawbone Health). Standard 12-lead electrocardiograms over-read by board-certified cardiac electrophysiologists were used as the reference standard. The accuracy of PPG signals to discriminate AF from sinus rhythm was evaluated by conventional measures of heart rate variability, a long short-term memory (LSTM) neural network given heart rate data only, and a deep convolutional-recurrent neural net (DNN) given the raw PPG data.
resultsFrom among 51 patients with persistent AF (age 63.6 ± 11.3 years; 78% male; 88% white), we randomly assigned 40 to train and 11 to test the algorithms. Whereas logistic regression analysis of heart rate variability yielded an area under the receiver operating characteristic curve (AUC) of 0.717 (sensitivity 0.741; specificity 0.584), the LSTM model given heart rate data exhibited AUC of 0.954 (sensitivity 0.810; specificity 0.921), and the DNN model given raw PPG data yielded the highest AUC of 0.983 (sensitivity 0.985; specificity 0.880).
conclusionA deep learning model given the raw PPG-based signal resulted in AF detection with high accuracy, performing better than conventional analyses relying on heart rate series data alone.
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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.