Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study (Preprint) Article Swipe
Soonil Kwon
,
Joonki Hong
,
Eue‐Keun Choi
,
Byunghwan Lee
,
Changhyun Baik
,
Euijae Lee
,
Eui–Rim Jeong
,
Bon‐Kwon Koo
,
Seil Oh
,
Yung Yi
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.16443
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.16443
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. CLINICALTRIAL ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188
Related Topics
Concepts
Photoplethysmogram
Atrial fibrillation
Medicine
Wearable computer
Cart
Sinus rhythm
Cardiology
Cardioversion
Internal medicine
Wearable technology
Electrocardiography
Pulse (music)
Prospective cohort study
Artificial intelligence
Computer science
Computer vision
Telecommunications
Engineering
Detector
Filter (signal processing)
Mechanical engineering
Embedded system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.2196/preprints.16443
- OA Status
- gold
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4254574479
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4254574479Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/preprints.16443Digital Object Identifier
- Title
-
Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study (Preprint)Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-09-30Full publication date if available
- Authors
-
Soonil Kwon, Joonki Hong, Eue‐Keun Choi, Byunghwan Lee, Changhyun Baik, Euijae Lee, Eui–Rim Jeong, Bon‐Kwon Koo, Seil Oh, Yung YiList of authors in order
- Landing page
-
https://doi.org/10.2196/preprints.16443Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2196/preprints.16443Direct OA link when available
- Concepts
-
Photoplethysmogram, Atrial fibrillation, Medicine, Wearable computer, Cart, Sinus rhythm, Cardiology, Cardioversion, Internal medicine, Wearable technology, Electrocardiography, Pulse (music), Prospective cohort study, Artificial intelligence, Computer science, Computer vision, Telecommunications, Engineering, Detector, Filter (signal processing), Mechanical engineering, Embedded systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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