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Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System
Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923104/ https://www.ncbi.nlm.nih.gov/pubmed/33671202 http://dx.doi.org/10.3390/s21041393 |
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author | George, Uduak Z. Moon, Kee S. Lee, Sung Q. |
author_facet | George, Uduak Z. Moon, Kee S. Lee, Sung Q. |
author_sort | George, Uduak Z. |
collection | PubMed |
description | Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm. |
format | Online Article Text |
id | pubmed-7923104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79231042021-03-03 Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System George, Uduak Z. Moon, Kee S. Lee, Sung Q. Sensors (Basel) Article Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm. MDPI 2021-02-17 /pmc/articles/PMC7923104/ /pubmed/33671202 http://dx.doi.org/10.3390/s21041393 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article George, Uduak Z. Moon, Kee S. Lee, Sung Q. Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System |
title | Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System |
title_full | Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System |
title_fullStr | Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System |
title_full_unstemmed | Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System |
title_short | Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System |
title_sort | extraction and analysis of respiratory motion using a comprehensive wearable health monitoring system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923104/ https://www.ncbi.nlm.nih.gov/pubmed/33671202 http://dx.doi.org/10.3390/s21041393 |
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