Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks

Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2021) Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks. PLoS ONE, 16 (4). e0249843.

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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.

Item ID: 67705
Item Type: Article (Research - C1)
ISSN: 1932-6203
Keywords: respiratory rate; electrocardiogram; photoplethysmogram; neural networks; machine learning; artificial intelligence
Copyright Information: Copyright: © 2021 Baker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funders: Commonwealth of Australia
Projects and Grants: Australian Postgraduate Award (APA)
Research Data:,
Date Deposited: 24 May 2021 02:27
FoR Codes: 40 ENGINEERING > 4003 Biomedical engineering > 400308 Medical devices @ 25%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50%
42 HEALTH SCIENCES > 4203 Health services and systems > 420309 Health management @ 25%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
20 HEALTH > 2001 Clinical health > 200199 Clinical health not elsewhere classified @ 50%
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