Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks
Open accessAbstract
Continuous and non-invasive respiratory rate (RR) monitoring would significantly improve patient outcomes. Currently, RR is under-recorded in clinical environments and is often measured by manually counting breaths. In this work, we investigate the use of respiratory signal quality quantification and several neural network (NN) structures for improved RR estimation. We extract respiratory modulation signals from the electrocardiogram (ECG) and photoplethysmogram (PPG) signals, and calculate a possible RR from each extracted signal. We develop a straightforward and efficient respiratory quality index (RQI) scheme that determines the quality of each moonddulation-extracted respiration signal. We then develop NNs for the estimation of RR, using estimated RRs and their corresponding quality index as input features. We determine that calculating RQIs for modulation-extracted RRs decreased the mean absolute error (MAE) of our NNs by up to 38.17%. When trained and tested using 60-sec waveform segments, the proposed scheme achieved an MAE of 0.638 breaths per minute. Based on these results, our scheme could be readily implemented into non-invasive wearable devices for continuous RR measurement in many healthcare applications.
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A key challenge for RR algorithms is to make them robust to signal artifact and only output values associated with a high degree of confidence, particularly when used with wearable data.
“A key challenge is to make algorithms robust to signal artifact and only output values associated with a high degree of confidence”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [148]
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- Authors
- Baker, S., Xiang, W., & Atkinson, I.
- Affiliations
- James Cook University (AU)
- La Trobe University (AU)
- Corresponding author
- Baker, Stephanie
- Xiang, Wei
- Atkinson, Ian
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-04-08
- Volume
- 16
- Issue
- 4
- Pages
- e0249843
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC8031461
- OpenAlex
- W3151990753
- Cited by
- 29
- References
- 31
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 2 · 2023: 12 · 2022: 2 · 2021: 1
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ResearchOnline at James Cook University (James Cook University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- La Trobe University (cc-by)
- Funders
- Australian Government Research Training Program Scholarship
- 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, photoplethysmogram, electrocardiogram, artificial neural networks, non-invasive respiratory monitoring, wearable devices
- MeSH terms
- Electrocardiography, Humans, Neural Networks, Computer, Photoplethysmography, Respiratory Rate
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