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Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography
Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and...
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/PMC10377422/ https://www.ncbi.nlm.nih.gov/pubmed/37504102 http://dx.doi.org/10.3390/bios13070703 |
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author | Naik, Ganesh R. Breen, Paul P. Jayarathna, Titus Tong, Benjamin K. Eckert, Danny J. Gargiulo, Gaetano D. |
author_facet | Naik, Ganesh R. Breen, Paul P. Jayarathna, Titus Tong, Benjamin K. Eckert, Danny J. Gargiulo, Gaetano D. |
author_sort | Naik, Ganesh R. |
collection | PubMed |
description | Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions. |
format | Online Article Text |
id | pubmed-10377422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103774222023-07-29 Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography Naik, Ganesh R. Breen, Paul P. Jayarathna, Titus Tong, Benjamin K. Eckert, Danny J. Gargiulo, Gaetano D. Biosensors (Basel) Article Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions. MDPI 2023-07-03 /pmc/articles/PMC10377422/ /pubmed/37504102 http://dx.doi.org/10.3390/bios13070703 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 Naik, Ganesh R. Breen, Paul P. Jayarathna, Titus Tong, Benjamin K. Eckert, Danny J. Gargiulo, Gaetano D. Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography |
title | Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography |
title_full | Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography |
title_fullStr | Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography |
title_full_unstemmed | Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography |
title_short | Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography |
title_sort | morphic sensors for respiratory parameters estimation: validation against overnight polysomnography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377422/ https://www.ncbi.nlm.nih.gov/pubmed/37504102 http://dx.doi.org/10.3390/bios13070703 |
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