Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review
Open accessAbstract
Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.
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Breathing influences the PPG signal at subcardiac frequencies through amplitude modulation, frequency modulation, and baseline wander.
“Breathing also influences the PPG signal at subcardiac frequencies in three ways (see the Supplementary Material): amplitude modulation, frequency modulation, and baseline wander [24].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [24]
For respiratory rate estimation, a much lower high-pass cutoff frequency is required (e.g., 0.05 Hz) to ensure that the lowest plausible respiratory rate of 4 bpm is preserved.
“In contrast, a much lower cutoff frequency is required for respiratory rate (RR) estimation (e.g., 0.05 Hz [24], ensuring that the lowest plausible RR of 4 bpm is preserved).”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [24]
RR can be estimated from subtle respiratory modulations in the PPG.
“RR, the number of breaths taken per minute, can be estimated from subtle respiratory modulations in the PPG”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [24]
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Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic
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Baker, S., Xiang, W., & Atkinson, I. (2021) · PLOS ONE · doi:10.1371/journal.pone.0249843 · Cited by 29 · see citing works
The quest for raw signals: a quality review of publicly available photoplethysmography datasets
Wolling, F., & Van Laerhoven, K. (2020) · Proceedings of the Third Workshop on Data: Acquisition To Analysis · doi:10.1145/3419016.3431485 · Cited by 9 · see citing works
- Authors
- Charlton, P. H., Birrenkott, D. A., Bonnici, T., Pimentel, M. A. F., Johnson, A. E. W., Alastruey, J., Tarassenko, L., Watkinson, P. J., Beale, R., & Clifton, D. A.
- Affiliations
- University of Oxford (GB)
- King's College London (GB)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Charlton, Peter H.
- ORCID
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2018
- Volume
- 11
- Pages
- 2-20
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- PMC
- PMC7612521
- OpenAlex
- W2760212863
- Cited by
- 379
- References
- 250
- Citations by year
- 2026: 34 · 2025: 54 · 2024: 40 · 2023: 59 · 2022: 53 · 2021: 54 · 2020: 43 · 2019: 29 · 2018: 12
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- UK Engineering and Physical Sciences Research Council (EP/H019944/1)
- Oxford and King's College London Centres of Excellence in Medical Engineering
- Wellcome Trust and EPSRC (WT88877/Z/09/Z, WT088641/Z/09/Z)
- Wellcome EPSRC Centre for Medical Engineering at Kings College London (WT 203148/Z/16/Z)
- National Institute for Health Research (NIHR) Biomedical Research Centre
- NHS Foundation Trust and King's College London
- NIHR Oxford Biomedical Research Centre Programme
- Royal Academy of Engineering Research
- 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
- respiratory rate estimation, electrocardiogram, photoplethysmogram, pulse oximetry, wearable sensors, remote video monitoring, algorithm performance evaluation, unobtrusive physiological monitoring
- MeSH terms
- Algorithms, Electrocardiography, Humans, Signal Processing, Computer-Assisted, Photoplethysmography, Respiratory Rate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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