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Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning
Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from hea...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951087/ https://www.ncbi.nlm.nih.gov/pubmed/35336250 http://dx.doi.org/10.3390/s22062079 |
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author | Stankoski, Simon Kiprijanovska, Ivana Mavridou, Ifigeneia Nduka, Charles Gjoreski, Hristijan Gjoreski, Martin |
author_facet | Stankoski, Simon Kiprijanovska, Ivana Mavridou, Ifigeneia Nduka, Charles Gjoreski, Hristijan Gjoreski, Martin |
author_sort | Stankoski, Simon |
collection | PubMed |
description | Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson’s correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device. |
format | Online Article Text |
id | pubmed-8951087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89510872022-03-26 Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning Stankoski, Simon Kiprijanovska, Ivana Mavridou, Ifigeneia Nduka, Charles Gjoreski, Hristijan Gjoreski, Martin Sensors (Basel) Article Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson’s correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device. MDPI 2022-03-08 /pmc/articles/PMC8951087/ /pubmed/35336250 http://dx.doi.org/10.3390/s22062079 Text en © 2022 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 Stankoski, Simon Kiprijanovska, Ivana Mavridou, Ifigeneia Nduka, Charles Gjoreski, Hristijan Gjoreski, Martin Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning |
title | Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning |
title_full | Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning |
title_fullStr | Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning |
title_full_unstemmed | Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning |
title_short | Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning |
title_sort | breathing rate estimation from head-worn photoplethysmography sensor data using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951087/ https://www.ncbi.nlm.nih.gov/pubmed/35336250 http://dx.doi.org/10.3390/s22062079 |
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