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An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation

The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Rece...

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Autores principales: Boccignone, Giuseppe, D’Amelio, Alessandro, Ghezzi, Omar, Grossi, Giuliano, Lanzarotti, Raffaella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098914/
https://www.ncbi.nlm.nih.gov/pubmed/37050444
http://dx.doi.org/10.3390/s23073387
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author Boccignone, Giuseppe
D’Amelio, Alessandro
Ghezzi, Omar
Grossi, Giuliano
Lanzarotti, Raffaella
author_facet Boccignone, Giuseppe
D’Amelio, Alessandro
Ghezzi, Omar
Grossi, Giuliano
Lanzarotti, Raffaella
author_sort Boccignone, Giuseppe
collection PubMed
description The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation.
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spelling pubmed-100989142023-04-14 An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation Boccignone, Giuseppe D’Amelio, Alessandro Ghezzi, Omar Grossi, Giuliano Lanzarotti, Raffaella Sensors (Basel) Article The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation. MDPI 2023-03-23 /pmc/articles/PMC10098914/ /pubmed/37050444 http://dx.doi.org/10.3390/s23073387 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Boccignone, Giuseppe
D’Amelio, Alessandro
Ghezzi, Omar
Grossi, Giuliano
Lanzarotti, Raffaella
An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation
title An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation
title_full An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation
title_fullStr An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation
title_full_unstemmed An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation
title_short An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation
title_sort evaluation of non-contact photoplethysmography-based methods for remote respiratory rate estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098914/
https://www.ncbi.nlm.nih.gov/pubmed/37050444
http://dx.doi.org/10.3390/s23073387
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