Signal quality measures for pulse oximetry through waveform morphology analysis
Open accessAbstract
Pulse oximetry has been extensively used to estimate oxygen saturation in blood, a vital physiological parameter commonly used when monitoring a subject's health status. However, accurate estimation of this parameter is difficult to achieve when the fundamental signal from which it is derived, the photoplethysmograph (PPG), is contaminated with noise artifact induced by movement of the subject or the measurement apparatus. This study presents a novel method for automatic rejection of artifact contaminated pulse oximetry waveforms, based on waveform morphology analysis. The performance of the proposed algorithm is compared to a manually annotated gold standard. The creation of the gold standard involved two experts identifying sections of the PPG signal containing good quality PPG pulses and/or noise, in 104 fingertip PPG signals, using a simultaneous electrocardiograph (ECG) signal as a reference signal. The fingertip PPG signals were each 1 min in duration and were acquired from 13 healthy subjects (10 males and 3 females). Each signal contained approximately 20 s of purposely induced artifact noise from a variety of activities involving subject movement. Some unique waveform morphology features were extracted from the PPG signals, which were believed to be correlated with signal quality. A simple decision-tree classifier was employed to arrive at a classification decision, at a pulse-by-pulse resolution, of whether a pulse was of acceptable quality for use or not. The performance of the algorithm was assessed using Cohen's kappa coefficient (κ), sensitivity, specificity and accuracy measures. A mean κ of 0.64 ± 0.22 was obtained, while the mean sensitivity, specificity and accuracy were 89 ± 10%, 77 ± 19% and 83 ± 11%, respectively. Furthermore, a heart rate estimate, extracted from uncontaminated sections of PPG, as identified by the algorithm, was compared with the heart rate derived from an uncontaminated simultaneous ECG signal. The mean error between both heart rate readings was 0.49 ± 0.66 beats per minute (BPM), in comparison to an error value observed without using the artifact detection algorithm of 7.23 ± 5.78 BPM. These results demonstrate that automated identification of signal artifact in the PPG signal through waveform morphology analysis is achievable. In addition, a clear improvement in the accuracy of the derived heart rate is also evident when such methods are employed.
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Among several pulse-to-pulse SQIs, only the one in [10] has been tested in long-term wrist PPGs acquired outside the laboratory setting.
“Out of several pulse-to-pulse SQIs [10,[49][50][51], only the one in [10] has been tested in long-term wrist PPGs acquired outside the laboratory setting and was therefore implemented in this study.”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [49]
Sukor et al.'s algorithm had an accuracy of 83 ± 11%, a sensitivity of 89 ± 10%, and a specificity of 77 ± 19%.
“They found their algorithm to have an accuracy of 83 ± 11%, a sensitivity of 89 ± 10 %, and a specificity of 77 ± 19%.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [169]
When comparing their algorithm to an ECG, Sukor et al. were able to lower the error in heart rate readings to 0.49 ± 0.66 bpm, whereas without the algorithm, the error was 7.23 ± 5.78 bpm.
“When comparing their algorithm to an ECG, they were able to lower the error in heart rate readings to 0.49 ± 0.66 bpm, whereas without the algorithm, the error was 7.23 ± 5.78 bpm.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [169]
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- Authors
- Sukor, J. A., Redmond, S. J., & Lovell, N. H.
- Affiliations
- UNSW Sydney (AU)
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2011-02-18
- Volume
- 32
- Issue
- 3
- Pages
- 369-384
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2068555665
- Cited by
- 166
- References
- 21
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 13 · 2023: 16 · 2022: 15 · 2021: 16 · 2020: 14 · 2019: 15 · 2018: 13 · 2017: 10 · 2016: 7 · 2015: 7 · 2014: 8 · 2013: 5 · 2012: 7
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- UTHM Institutional Repository (Universiti Tun Hussein Onn Malaysia) (acceptedVersion)
- 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
- pulse oximetry, photoplethysmography, signal quality assessment, artifact detection, waveform morphology, decision tree classification, heart rate estimation, oxygen saturation, motion artifacts
- MeSH terms
- Adult, Algorithms, Electrocardiography, Female, Heart Rate, Humans, Male, Oximetry, Reference Values, User-Computer Interface, Artifacts, Photoplethysmography, Wavelet Analysis
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