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Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera
The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980749/ https://www.ncbi.nlm.nih.gov/pubmed/33743106 http://dx.doi.org/10.1007/s10877-021-00691-3 |
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author | Addison, Paul S. Smit, Philip Jacquel, Dominique Addison, Anthony P. Miller, Cyndy Kimm, Gardner |
author_facet | Addison, Paul S. Smit, Philip Jacquel, Dominique Addison, Anthony P. Miller, Cyndy Kimm, Gardner |
author_sort | Addison, Paul S. |
collection | PubMed |
description | The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a ventilator reference. Depth sensing data streams were acquired and processed over a series of runs on a single volunteer comprising a range of respiratory rates and tidal volumes to generate depth-based respiratory rate (RR(depth)) and tidal volume (TV(depth)) estimates. The bias and root mean squared difference (RMSD) accuracy between RR(depth) and the ventilator reference, RR(vent), across the whole data set was found to be -0.02 breaths/min and 0.51 breaths/min respectively. The least squares fit regression equation was determined to be: RR(depth) = 0.96 × RR(vent) + 0.57 breaths/min and the resulting Pearson correlation coefficient, R, was 0.98 (p < 0.001). Correspondingly, the bias and root mean squared difference (RMSD) accuracy between TV(depth) and the reference TV(vent) across the whole data set was found to be − 0.21 L and 0.23 L respectively. The least squares fit regression equation was determined to be: TV(depth) = 0.79 × TV(vent)—0.01 L and the resulting Pearson correlation coefficient, R, was 0.92 (p < 0.001). In conclusion, a high degree of agreement was found between the depth-based respiration rate and its ventilator reference, indicating that RR(depth) is a promising modality for the accurate non-contact respiratory rate monitoring in the clinical setting. In addition, a high degree of correlation between depth-based tidal volume and its ventilator reference was found, indicating that TV(depth) may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting. |
format | Online Article Text |
id | pubmed-7980749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-79807492021-03-23 Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera Addison, Paul S. Smit, Philip Jacquel, Dominique Addison, Anthony P. Miller, Cyndy Kimm, Gardner J Clin Monit Comput Original Research The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a ventilator reference. Depth sensing data streams were acquired and processed over a series of runs on a single volunteer comprising a range of respiratory rates and tidal volumes to generate depth-based respiratory rate (RR(depth)) and tidal volume (TV(depth)) estimates. The bias and root mean squared difference (RMSD) accuracy between RR(depth) and the ventilator reference, RR(vent), across the whole data set was found to be -0.02 breaths/min and 0.51 breaths/min respectively. The least squares fit regression equation was determined to be: RR(depth) = 0.96 × RR(vent) + 0.57 breaths/min and the resulting Pearson correlation coefficient, R, was 0.98 (p < 0.001). Correspondingly, the bias and root mean squared difference (RMSD) accuracy between TV(depth) and the reference TV(vent) across the whole data set was found to be − 0.21 L and 0.23 L respectively. The least squares fit regression equation was determined to be: TV(depth) = 0.79 × TV(vent)—0.01 L and the resulting Pearson correlation coefficient, R, was 0.92 (p < 0.001). In conclusion, a high degree of agreement was found between the depth-based respiration rate and its ventilator reference, indicating that RR(depth) is a promising modality for the accurate non-contact respiratory rate monitoring in the clinical setting. In addition, a high degree of correlation between depth-based tidal volume and its ventilator reference was found, indicating that TV(depth) may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting. Springer Netherlands 2021-03-20 2022 /pmc/articles/PMC7980749/ /pubmed/33743106 http://dx.doi.org/10.1007/s10877-021-00691-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Addison, Paul S. Smit, Philip Jacquel, Dominique Addison, Anthony P. Miller, Cyndy Kimm, Gardner Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera |
title | Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera |
title_full | Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera |
title_fullStr | Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera |
title_full_unstemmed | Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera |
title_short | Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera |
title_sort | continuous non‐contact respiratory rate and tidal volume monitoring using a depth sensing camera |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980749/ https://www.ncbi.nlm.nih.gov/pubmed/33743106 http://dx.doi.org/10.1007/s10877-021-00691-3 |
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