Developing an algorithm for pulse oximetry derived respiratory rate (RRoxi): a healthy volunteer study
Open accessAbstract
OBJECTIVE: The presence of respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring respiratory rate requires: (1) the recognition of the multi-faceted manifestations of respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported respiratory rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RR(OXI) algorithm based on this principle and its performance on healthy subject trial data is described herein. METHODS: Finger PPGs were collected from a cohort of 139 healthy adult volunteers monitored during free breathing over an 8-min period. These were subsequently processed using a novel in-house algorithm based on continuous wavelet transform technology within an infrastructure incorporating weighted averaging and logical decision making processes. The computed oximeter respiratory rates (RR(oxi)) were then compared to an end-tidal CO2 reference rate RR(ETCO2). RESULTS: RR(ETCO2) ranged from a lowest recorded value of 2.97 breaths per min (br/min) to a highest value of 28.02 br/min. The mean rate was 14.49 br/min with standard deviation of 4.36 br/min. Excellent agreement was found between RR(oxi) and RR(ETCO2), with a mean difference of -0.23 br/min and standard deviation of 1.14 br/min. The two measures are tightly spread around the line of agreement with a strong correlation observable between them (R2 = 0.93). CONCLUSIONS: These data indicate that RR(oxi) represents a viable technology for the measurement of respiratory rate of healthy individuals.
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RIIV modulates the DC baseline of the PPG curve superimposing the wave on top of a low frequency sinusoid.
“RIIV modulates the DC baseline of the PPG curve superimposing the wave on top of a low frequency sinusoid.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [121]
RIFV induces a phase shift between each cycle by elongating or squeezing each wave.
“RIFV induces a phase shift between each cycle by elongating or squeezing each wave.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [121]
RIAV causes significant changes in each peak amplitude.
“RIAV causes significant changes in each peak amplitude.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [121]
Dehkordi et al. and Addison et al. identified three significant variations to the PPG signal by respiratory rate: respiratory-induced intensity variation (RIIV), respiratory-induced amplitude variation (RIAV), and respiratory induced frequency variation (RIFV).
“Dehkordi et al. and Addison et al. identified the three significant variations to the PPG signal by respiratory rate after analyzing PPG from 139 healthy adults (67–18 years old) and the Capnobase dataset [115,121]. These variations are respiratory-induced intensity variation (RIIV), respiratory-induced amplitude variation (RIAV), and respiratory induced frequency variation (RIFV), as shown in FIG”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [121]
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- Authors
- Addison, P. S., Watson, J. N., Mestek, M. L., & Mecca, R. S.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Addison, Paul S.
- Venue
- Journal of Clinical Monitoring and Computing
- Published
- 2012-01-10
- Volume
- 26
- Issue
- 1
- Pages
- 45-51
- ISSN
- 1387-1307 (ISSN-L)
- 1573-2614
- PMC
- PMC3268017
- OpenAlex
- W2014322746
- Cited by
- 97
- References
- 24
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 6 · 2022: 11 · 2021: 4 · 2020: 10 · 2019: 7 · 2018: 8 · 2017: 9 · 2016: 13 · 2015: 5 · 2014: 9 · 2013: 4 · 2012: 5
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Clinical Monitoring and Computing (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- 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
- pulse oximetry, respiratory rate, photoplethysmography, continuous wavelet transform, end-tidal carbon dioxide, non-invasive respiratory monitoring, signal processing, healthy volunteers
- MeSH terms
- Adolescent, Adult, Aged, Algorithms, Female, Humans, Male, Middle Aged, Monitoring, Physiologic, Oximetry, Reference Values, Signal Processing, Computer-Assisted, Body Mass Index, Young Adult, Respiratory Rate, Wavelet Analysis
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