Atrial fibrillation detection from wrist photoplethysmography signals using smartwatches
Open accessAbstract
Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets.
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A common way to deal with artifact-corrupted PPGs is to exclude segments of poor signal quality and accelerometer-identified motion.
“A common way to deal with artifact-corrupted PPGs, and thus improve the reliability of arrhythmia detectors, is to exclude segments of poor signal quality [10,27,28] as well as accelerometer-identified motion [11][12][13][27][28][29][30].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [27]
Accelerometer-based artifact identification may be ineffective in certain situations because artifacts may arise from other sources, such as poor contact and fine hand movements, which do not reflect in accelerometer signals.
“Such approach may be ineffective in certain situations since artifacts may arise from other sources as well, e.g., poor contact and fine hand movements, which do not reflect in the accelerometer signals [23,27].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [27]
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- Authors
- Bashar, S. K., Han, D., Hajeb-Mohammadalipour, S., Ding, E., Whitcomb, C., McManus, D. D., & Chon, K. H.
- Affiliations
- University of Connecticut (US)
- University of Massachusetts Chan Medical School (US)
- Corresponding author
- Chon, Ki H.
- ORCID
- Venue
- Scientific Reports
- Published
- 2019-10-21
- Volume
- 9
- Issue
- 1
- Pages
- 15054-15054
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6803677
- OpenAlex
- W2980628905
- Cited by
- 120
- References
- 25
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 15 · 2023: 17 · 2022: 32 · 2021: 15 · 2020: 10 · 2019: 2
- FWCI
- 5.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship@UMassChan (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (1522087)
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL137794, R01HL137734)
- 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, smartwatches, wrist-worn sensors, motion artifact detection, accelerometer, time-frequency analysis, sample entropy, RMSSD, premature atrial contractions, arrhythmia monitoring, beat-to-beat intervals
- MeSH terms
- Algorithms, Atrial Fibrillation, Electrocardiography, Heart Rate, Humans, Motion, Noise, ROC Curve, Signal Processing, Computer-Assisted, Wrist, Artifacts, Photoplethysmography, Entropy, Databases as Topic
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