Evaluation of the signal quality of wrist-based photoplethysmography
Open accessAbstract
Objective : Wearable devices with embedded photoplethysmography (PPG) sensors enable continuous monitoring of cardiovascular activity, allowing for the detection cardiovascular problems, such as arrhythmias. However, the quality of wrist-based PPG is highly variable, and is subject to artifacts from motion and other interferences. The goal of this paper is to evaluate the signal quality obtained from wrist-based PPG when used in an ambulatory setting. Approach : Ambulatory data were collected over a 24 h period for 10 elderly, and 16 non-elderly participants. Visual assessment is used as the gold standard for PPG signal quality, with inter-rater agreement evaluated using Fleiss’ Kappa. With this gold standard, 5 classifiers were evaluated using a modified 13-fold cross-validation approach. Main results : A Random Forest quality classification algorithm showed the best performance, with an accuracy of 74.5%, and was then used to evaluate 24 h long ambulatory wrist-based PPG measurements. Significance : In general, data quality was high at night, and low during the day. Our results suggest wrist-based PPG may be best for continuous cardiovascular monitoring applications during the night, but less useful during the day unless methods can be identified to improve low quality signal segments.
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Signal quality assessments have been done under more general circumstances, but wearable data quality in the context of epilepsy has not been a widely researched topic so far.
“Signal quality assessments have been done under more general circumstances10,34–37, but wearable data quality in the context of epilepsy has not been a widely researched topic so far8,9,38.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [35]
The finding that overall data quality is higher during nighttime in the authors' large cohort is in line with findings in smaller samples specifically for PPG data.
“This finding in our large cohort is in line with findings in smaller samples specifically for PPG data35,75.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [35]
The percentage of the artifact-corrupted PPG markedly increases during day-time, mostly due to device displacement and forearm motion.
“In line with recent findings [48], our study shows that the percentage of the artifact-corrupted PPG markedly increases during day-time, mostly due to device displacement and forearm motion.”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [48]
In the ambulatory database, a quarter of the PPG duration was identified as corrupted by artifacts, which is higher than 10.8% reported in [10], but much lower than 44% in [12] and 65.2% in [48].
“In the ambulatory database, a quarter of the PPG duration was identified as corrupted by artifacts, which is higher than 10.8% reported in [10], but much lower than 44% in [12] and 65.2% in [48].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [48]
No artifact type-specific characteristics valuable for the model development were given in that study.
“Yet, no artifact type-specific characteristics valuable for the model development were given.”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [48]
The quality of a long-term green-light PPG acquired using a smartwatch was investigated in another study.
“In another study, the quality of a long-term green-light PPG, acquired using a smartwatch was investigated [48].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [48]
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- Authors
- Pradhan, N., Rajan, S., & Adler, A.
- Affiliations
- Carleton University (CA)
- Corresponding author
- Adler, Andy
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2019-06-01
- Volume
- 40
- Issue
- 6
- Pages
- 065008
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2945703677
- Cited by
- 50
- References
- 24
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 2 · 2023: 12 · 2022: 6 · 2021: 8 · 2020: 3 · 2019: 1
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- photoplethysmography, signal quality assessment, wearable sensors, ambulatory monitoring, motion artifacts, arrhythmia detection, cardiovascular monitoring, older adults, nocturnal monitoring, random forest classification
- MeSH terms
- Algorithms, Humans, Signal Processing, Computer-Assisted, Time Factors, Wrist, Photoplethysmography, Accelerometry
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