Cited by 348Non-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic ControlHemodynamic Monitoring and Therapy

The DOI matches its Crossref record; 210 of the 212 references it cites were found in a registry or an index; 3 of the 283 sources its claims cite were checked for support.

Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring

Open access

Abstract

Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review synthesizes literature on sources of inaccuracy in photoplethysmography (PPG) for continuous cardiovascular monitoring, grouping them into individual variation, physiological processes, and external perturbations. It argues that advanced PPG parameters require accounting for these noise sources across diverse populations and real-world conditions, not just controlled fitness/heart-rate settings.

Key findings

  • Noise sources are divided into three main categories: individual variation (skin tone, obesity, age, gender), physiology (respiration, venous pulsation, body site, local body temperature), and external perturbation (motion artifact, ambient light, applied pressure).
  • Skin tone: a 535 nm wrist device showed error of 1.04 BPM for “fair” skin vs 10.90 BPM for “dark” skin; Polar OH1 bias increased with darker skin (p < 0.001), while Bent et al. found no statistically significant skin-tone relationship but a mean absolute error of approximately 9 BPM across all skin tones.
  • Obesity: Monte Carlo simulations predicted a 40% loss of PPG signal amplitude on the wrist as simulated radial artery depth increased from 2.5 to 3.5 mm; obesity is also linked to changes in capillary density, blood flow, SpO2, trans epidermal water loss, and skin thickness.
  • Motion and pressure: Basis Peak vs Fitbit Charge HR correlations were r = 0.92 vs r = 0.83 overall, and during high-intensity events r = 0.77 vs r = 0.58; Apple Watch 4 had MAE of 2.7 BPM at rest and 4.6 BPM while walking. Applied pressure from 0–15 kPa produced over 300% variation in the b/a ratio, dropping to less than 5% at optimal pressure; a force regulator reduced coefficient of variation from 50.9% to 1.8%.

Methods

  • Design: comprehensive literature review; no new experimental data are reported.
  • Data/sample: synthesizes published human studies, in silico modeling, commercial and FDA-cleared/approved PPG devices, and prior reviews; examples include studies with 256 subjects for skin tone, 70 subjects for Polar OH1, 56 patients for wearable comparison, and 93 individuals for age-related PPG features.
  • Main analysis: narrative synthesis categorizing noise sources into individual variation, physiology, and external factors, with summary tables for age-induced PPG changes, temperature effects, and adaptive filters for motion artifacts.

Limitations

  • The paper states that few noise sources had a comprehensive solution, understanding of how conditions such as obesity affect skin and cardiovascular monitoring is still developing, and future research should incorporate larger studies inclusive of diverse populations.
  • Evident from the text: as a narrative review, it does not report a systematic search strategy or quantitative meta-analysis; many cited studies are small, some lack statistical analyses, and findings conflict (e.g., Bent et al. vs other skin-tone studies; inconsistent skin thickness data).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/bios11040126

Cites

210 of 210 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 3 (a) Absorption coefficient of epidermal melanin vs. bulk epidermis. (b) Optical absorption of oxygenated hemoglobin and water [47,48,49,50].
Figure 3 (a) Absorption coefficient of epidermal melanin vs. bulk epidermis. (b) Optical absorption of oxygenated hemoglobin and water [47,48,49,50].CC BY · PubMed Central
Figure 1 (a) Photoplethysmography (PPG) waveform: 1. systolic peak; 2. dicrotic notch; 3. diastolic peak; 4. slope transit time; 5. heart rate; 6. area under systolic waveform; 7. area under diastolic waveform; (b) first derivative, velocity plethysmograph (VPG): 8. max slope in systole; 9. end of systolic peak; 10. Start of dicrotic notch; 11. max slope in diastole; (c) second derivative, second derivative photoplethysmograph or acceleration plethysmograph (SDPPG/APG): 12. a-point; 13. b-point; 14. c-point; 15. d-point; 16. e-point. Created with BioRender.com.
Figure 1 (a) Photoplethysmography (PPG) waveform: 1. systolic peak; 2. dicrotic notch; 3. diastolic peak; 4. slope transit time; 5. heart rate; 6. area under systolic waveform; 7. area under diastolic waveform; (b) first derivative, velocity plethysmograph (VPG): 8. max slope in systole; 9. end of systolic peak; 10. Start of dicrotic notch; 11. max slope in diastole; (c) second derivative, second derivative photoplethysmograph or acceleration plethysmograph (SDPPG/APG): 12. a-point; 13. b-point; 14. c-point; 15. d-point; 16. e-point. Created with BioRender.com.CC BY · PubMed Central
Figure 2 The Fitzpatrick Scale, ranging from I (little epidermal melanin, ~3% volume fraction melanosomes) to VI (significant epidermal melanin, ~43% volume fraction melanosomes) [39].
Figure 2 The Fitzpatrick Scale, ranging from I (little epidermal melanin, ~3% volume fraction melanosomes) to VI (significant epidermal melanin, ~43% volume fraction melanosomes) [39].CC BY · PubMed Central
Figure 4 Five primary points of interest in an acceleration plethysmograph (PPG 2nd derivative). Created with BioRender.com.
Figure 4 Five primary points of interest in an acceleration plethysmograph (PPG 2nd derivative). Created with BioRender.com.CC BY · PubMed Central
Figure 5 Respiratory-induced intensity variation (RIIV), respiratory-induced amplitude variation (RIAV), and respiratory-induced frequency variation (RIFV) respiratory-influenced variation to PPG signal [115].
Figure 5 Respiratory-induced intensity variation (RIIV), respiratory-induced amplitude variation (RIAV), and respiratory-induced frequency variation (RIFV) respiratory-influenced variation to PPG signal [115].CC BY · PubMed Central
Figure 6 Venous pulse within a PPG. The blue waveform is the venous waveform, and the red waveform shows the PPG waveform. Adapted from Shelley et al. [116]. Created with BioRender.com.
Figure 6 Venous pulse within a PPG. The blue waveform is the venous waveform, and the red waveform shows the PPG waveform. Adapted from Shelley et al. [116]. Created with BioRender.com.CC BY · PubMed Central
Figure 7 Relative total skin thicknesses across body sites for a sample of studies, from thinnest to thickest. Each anatomy also has (1), (2), or (3) next to its name, indicating the number of studies this body site was measured in (out of 5 considered works). Created with BioRender.com [80,81,140,142,143].
Figure 7 Relative total skin thicknesses across body sites for a sample of studies, from thinnest to thickest. Each anatomy also has (1), (2), or (3) next to its name, indicating the number of studies this body site was measured in (out of 5 considered works). Created with BioRender.com [80,81,140,142,143].CC BY · PubMed Central

Verification

Claims the paper makes

201 Not checked yet · 51 Own interpretation · 23 No basis given · 2 Supported · 1 Own result

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Show all 152 claims

  • Probably not a substantial claim. 40
  • It may state two separate claims as one. 102
  • The quote only partly states this claim. 1
126 of 126
  • The paper relies on this without support.

    The findings of the cited studies on specific PPG devices and populations are applicable to PPG-based continuous cardiovascular monitoring devices in general.

    “Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces.”
  • The paper relies on this without support.

    The Fitzpatrick scale is a valid proxy for skin pigmentation relevant to PPG light-tissue interactions.

    “The most common way to characterize skin tone is via the Fitzpatrick Scale [37].”
  • The paper relies on this without support.

    The physiological changes associated with obesity measured in the cited studies are present in the target population of PPG users.

    “The variation of BMI across individuals is thus a potential source of variation for PPG measurements.”
  • The paper relies on this without support.

    Gender is binary.

    “While the wide spectrum of ages can make it a difficult parameter to incorporate into models, gender is binary, making it easier to incorporate.”
  • The paper relies on this without support.

    PWV is highly dependent on vascular stiffness and is observable through PPG.

    “PWV is highly dependent on vascular stiffness and is observable through PPG.”
  • The paper relies on this without support.

    Pulse pressure is dependent on both arterial stiffness and cardiac output.

    “Pulse pressure is dependent on both arterial stiffness and cardiac output.”
  • The paper relies on this without support.

    Motion artifacts are picked up by PPG sensors and cause fluctuations within the collected signal.

    “Within all settings, motion artifacts are picked up by the sensor and cause fluctuations within the collected signal.”
  • The paper relies on this without support.

    Ambient light intensity is generally multiple orders of magnitude larger than the pulsatile (AC) component of the PPG, which can lead to signal saturation.

    “The ambient light intensity can be a zero frequency (e.g., DC), such as sunlight, or at a variable frequency, as from room lights (e.g., 60 Hz in the US) and is generally multiple orders of magnitude larger than the pulsatile (AC) component of the PPG, which can lead to signal saturation.”
  • The paper relies on this without support.

    Only a limited number of RCIM PPG devices have FDA approval.

    “However, only a limited number of RCIM PPG devices have FDA approval.”
9 of 9

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Self-check

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3 of 3

Citations

Cites

1 retracted

Show all 212 references

Research works printed but not linked: 3

  1. Hasan, 2015, A New First-Derivative Related Index to Assess Pulse Wave Transit Time from a Photoplethysmographic Waveform: Age Dependence and Agreement with Normative Data, Int. J. Bioinform. Biomed. Eng., 1, 276
  2. Goepfert, L., Petersen, C.L., Ansermino, J.M., and Gorges, M. (2018, January 10–13). Evaluation of a Combined PPG/MPG Sensor in Healthy Volunteers. Proceedings of the Anesthesia and Analgesia, Miami, FL, USA.
  3. Sazonov, E. (2014). Wearable Sensors: Fundamentals, Implementation and Applications, Elsevier.

Cited by

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Metadata

Authors
Fine, J., Branan, K. L., Rodriguez, A. J., Boonya-ananta, T., Ajmal, Ramella-Roman, J. C., McShane, M. J., & Coté, G. L.
Affiliations
  • Texas A&M University (US)
  • Florida International University (US)
  • Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA
  • Texas A&M Engineering Experiment Station (US)
Corresponding author
  • McShane, Michael J.
  • Coté, Gerard L.
Venue
Biosensors
Publisher
MDPI AG
Published
2021-04-16
Volume
11
Issue
4
Pages
126
ISSN
2079-6374 (ISSN-L)
OpenAlex
W3155015885
Cited by
348
References
212
Citations by year
2026: 80 · 2025: 74 · 2024: 70 · 2023: 74 · 2022: 40 · 2021: 10
FWCI
4.09
Type
journal article
Language
en
Open access
Locations
  • Biosensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Florida International University Digital Commons (Florida International University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Science Foundation (1648451)
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 › Surgery › Hemodynamic Monitoring and Therapy
Keywords
photoplethysmography, wearable health monitoring, cardiovascular monitoring, motion artifacts, ambient light interference, skin tone, body temperature, heart rate variability, blood pressure estimation, respiration, venous pulsation
MeSH terms
Wearable Electronic Devices, Blood Pressure, Heart Rate, Humans, Monitoring, Physiologic, Respiration, Signal Processing, Computer-Assisted, Artifacts, Photoplethysmography

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