Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study
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Abstract
Background: Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective: We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Methods: Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). Results: In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. Conclusions: A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. Trial Registration: ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188
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Wearables are applied in diverse fields of health.
“Apart from these 2 examples, wearables are applied in diverse fields of health, including acoustic, gastrointestinal sensors for ileus prediction [18]; UV sun exposure [19]; heat-related illness measurements [20]; electrolyte monitoring, for example, for cystic fibrosis or training management [21,22]; early warning of AF with a wearable ring [23]; generation of electrocardiograms (ECGs) [15];”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [23]
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Self-citation
- Authors
- Kwon, S., Hong, J., Choi, E.-K., Lee, B., Baik, C., Lee, E., Jeong, E.-R., Koo, B.-K., Oh, S., & Yi, Y.
- Affiliations
- Seoul National University Hospital (KR)
- Korea Advanced Institute of Science and Technology (KR)
- Hanbat National University (KR)
- Corresponding author
- Choi, Eue-Keun
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2020-05-21
- Volume
- 22
- Issue
- 5
- Pages
- e16443
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC7273241
- OpenAlex
- W3012640303
- Cited by
- 65
- References
- 30
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 9 · 2023: 16 · 2022: 10 · 2021: 13 · 2020: 5 · 2019: 1
- FWCI
- 3.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Seoul National University
- Seoul National University Hospital
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Keywords
- atrial fibrillation detection, photoplethysmography, deep learning, cardioversion, persistent atrial fibrillation, continuous cardiac monitoring, pulse oximeter comparison, external validation, smartphone-based diagnostics
- MeSH terms
- Proof of Concept Study, Wearable Electronic Devices, Deep Learning, Atrial Fibrillation, Female, Humans, Male, Middle Aged, Prospective Studies, Photoplethysmography
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