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Optical Monitoring of Breathing Patterns and Tissue Oxygenation: A Potential Application in COVID-19 Screening and Monitoring Mah, Aaron James; Nguyen, Thien; Ghazi Zadeh, Leili; Shadgan, Atrina; Khaksari, Kosar; Nourizadeh, Mehdi; Zaidi, Ali; Park, Soongho; Gandjbakhche, Amir H.; Shadgan, Babak

Abstract

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O2Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.

Item Metadata

Title
Optical Monitoring of Breathing Patterns and Tissue Oxygenation: A Potential Application in COVID-19 Screening and Monitoring
Creator
Mah, Aaron James; Nguyen, Thien; Ghazi Zadeh, Leili; Shadgan, Atrina; Khaksari, Kosar; Nourizadeh, Mehdi; Zaidi, Ali; Park, Soongho; Gandjbakhche, Amir H.; Shadgan, Babak
Contributor
International Collaboration on Repair Discoveries
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2022-09-26
Description
The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O2Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.
Subject
COVID-19 screening; COVID-19 monitoring; pneumonia; respiratory disease; NIRS; optical monitoring; wearable biosensor; respiratory monitoring; breathing patterns; tissue oxygenation
Genre
Article
Type
Text
Language
eng
Date Available
2024-11-12
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0447250
URI
http://hdl.handle.net/2429/89643
Affiliation
Applied Science, Faculty of; Medicine, Faculty of; Non UBC; Biomedical Engineering, School of; Orthopaedics, Department of; Pathology and Laboratory Medicine, Department of
Citation
Sensors 22 (19): 7274 (2022)
Publisher DOI
10.3390/s22197274
Peer Review Status
Reviewed
Scholarly Level
Faculty; Researcher; Other
Rights URI
https://creativecommons.org/licenses/by/4.0/
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CC BY 4.0