Photoplethysmography based atrial fibrillation detection: a review
Open accessAbstract
Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.
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AF can be identified from pulse wave signals due to the irregular heart rhythm produced by AF.
“atrial fibrillation (AF), which can be identified from pulse wave signals due to the irregular heart rhythm produced by AF”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [27]
Several techniques have been proposed to assess the irregularity of IBIs and the shape of pulse waves in order to detect AF from the PPG.
“Several techniques have been proposed to assess the irregularity of IBIs and the shape of pulse waves in order to detect AF from the PPG”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [27]
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- Authors
- Pereira, T., Tran, N., Gadhoumi, K., Pelter, M. M., Do, D. H., Lee, R. J., Colorado, R., Meisel, K., & Hu, X.
- Affiliations
- University of California, San Francisco (US)
- University of California, Los Angeles (US)
- Venue
- npj Digital Medicine
- Published
- 2020-01-10
- Volume
- 3
- Issue
- 1
- Pages
- 3-3
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6954115
- OpenAlex
- W2999926771
- Cited by
- 300
- References
- 102
- Citations by year
- 2026: 34 · 2025: 47 · 2024: 57 · 2023: 51 · 2022: 40 · 2021: 50 · 2020: 18 · 2019: 1 · 2016: 1
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (R01NHLBI128679)
- National Heart, Lung, and Blood Institute (R01NHLBI128679, R01HL128679)
- 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 › Heart Rate Variability and Autonomic Control
- Keywords
- atrial fibrillation detection, photoplethysmography, wearable devices, optical sensors, cardioembolic stroke prevention, signal processing, machine learning, deep learning, early screening
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