Methods to Estimate Respiratory Rate Using the Photoplethysmography Signal

Ayalon Moraes Filho, Guilherme Schreiber, Julio Sieg, Maicon Much, Vanessa Bartoski, César Marcon

2023

Abstract

Academia and industry have devoted significant effort to the research and development of smart wearable devices applied to health monitoring. The photoplethysmography (PPG) sensor is widely used for monitoring biosignals, such as heart and respiratory rate (RR), which are influenced by the cardiovascular system. This work focuses on analyzing methods for RR estimation regarding the effect of breathing on the PPG signal variation. This work describes, implements, and analyzes four methods for estimating RR. These methods are based on capturing RR using Fast Fourier Transform, median, and extracting physiological characteristics induced by respiration in the PPG signal. The most efficient method merges three RR calculations analyzed on the same signal, achieving nearly 93% of efficacy in the best scenario. The method efficacies were calculated using PPG signals from the BIDMC and CapnoBase databases collected from patients during hospital care. The analysis allows for understanding and mitigating the RR estimation challenges and evaluating the most efficacy method for a wearable device monitoring scenario.

Paper Citation

in Harvard Style

Moraes Filho A., Schreiber G., Sieg J., Much M., Bartoski V. and Marcon C. (2023). Methods to Estimate Respiratory Rate Using the Photoplethysmography Signal. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 445-452. DOI: 10.5220/0011729100003414

in Bibtex Style

@conference{healthinf23,
author={Ayalon Moraes Filho and Guilherme Schreiber and Julio Sieg and Maicon Much and Vanessa Bartoski and César Marcon},
title={Methods to Estimate Respiratory Rate Using the Photoplethysmography Signal},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={445-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011729100003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Methods to Estimate Respiratory Rate Using the Photoplethysmography Signal
SN - 978-989-758-631-6
AU - Moraes Filho A.
AU - Schreiber G.
AU - Sieg J.
AU - Much M.
AU - Bartoski V.
AU - Marcon C.
PY - 2023
SP - 445
EP - 452
DO - 10.5220/0011729100003414
PB - SciTePress