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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10098914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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