Ying Xuan Zhi1, Daniel Vena2, Milos R Popovic3, T Douglas Bradley4, Azadeh Yadollahi5. 1. University Health Network, Toronto Rehabilitation Institute, 550 University Ave., Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada. Electronic address: derek.zhi@mail.utoronto.ca. 2. University Health Network, Toronto Rehabilitation Institute, 550 University Ave., Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada. Electronic address: dvena@bwh.harvard.edu. 3. University Health Network, Toronto Rehabilitation Institute, 550 University Ave., Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada. Electronic address: milos.popovic@uhn.ca. 4. University Health Network, Toronto Rehabilitation Institute, 550 University Ave., Toronto, Canada; Centre for Sleep Medicine and Circadian Biology, University of Toronto, Toronto, Canada; Department of Medicine, University Health Network, Toronto General Hospital, Toronto, Canada. Electronic address: douglas.bradley@uhn.ca. 5. University Health Network, Toronto Rehabilitation Institute, 550 University Ave., Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada. Electronic address: azadeh.yadollahi@uhn.ca.
Abstract
BACKGROUND: Inspiratory flow limitation is a breathing pattern during sleep caused by upper airway (UA) narrowing that occurs during snoring and various degrees of obstructive sleep apnea (OSA). Clinical examination of flow limitation relies on identifying patterns of airflow contour, however this process is subjective and lacks physiological evidence of UA narrowing. Our objective is to derive the temporal features of nasal airflow contour that characterize flow limitation. The features that correlate with UA narrowing can be used to develop machine learning classifiers to detect flow limitation with physiological support. METHODS: Sixteen healthy adult men underwent full daytime polysomnography where the nasal airflow was recorded. Before and after sleep, we measured UA anatomical parameters including neck circumference (NC) and upper-airway cross-sectional area (UA-XSA). We extracted various temporal features of airflow and investigated their relationships with the UA anatomical parameters. RESULTS: We found that three features were correlated with the anatomical parameters associated with UA narrowing: deviation index vs. baseline UA-XSA (r = -0.67, p = 0.01), peak amplitude variability vs. baseline UA-XSA (r = -0.69, p < 0.01), peak amplitude variability vs. ΔNC (r = 0.74, p < 0.01) and peak number vs. baseline UA-XSA (r = -0.54, p = 0.04). CONCLUSIONS: Temporal features of airflow were associated with UA narrowing. Future studies could utilize the features to develop classifiers to detect flow limitation and assess the severity of breathing disorders during sleep in high-risk populations such as pregnant women and children.
BACKGROUND: Inspiratory flow limitation is a breathing pattern during sleep caused by upper airway (UA) narrowing that occurs during snoring and various degrees of obstructive sleep apnea (OSA). Clinical examination of flow limitation relies on identifying patterns of airflow contour, however this process is subjective and lacks physiological evidence of UA narrowing. Our objective is to derive the temporal features of nasal airflow contour that characterize flow limitation. The features that correlate with UA narrowing can be used to develop machine learning classifiers to detect flow limitation with physiological support. METHODS: Sixteen healthy adult men underwent full daytime polysomnography where the nasal airflow was recorded. Before and after sleep, we measured UA anatomical parameters including neck circumference (NC) and upper-airway cross-sectional area (UA-XSA). We extracted various temporal features of airflow and investigated their relationships with the UA anatomical parameters. RESULTS: We found that three features were correlated with the anatomical parameters associated with UA narrowing: deviation index vs. baseline UA-XSA (r = -0.67, p = 0.01), peak amplitude variability vs. baseline UA-XSA (r = -0.69, p < 0.01), peak amplitude variability vs. ΔNC (r = 0.74, p < 0.01) and peak number vs. baseline UA-XSA (r = -0.54, p = 0.04). CONCLUSIONS: Temporal features of airflow were associated with UA narrowing. Future studies could utilize the features to develop classifiers to detect flow limitation and assess the severity of breathing disorders during sleep in high-risk populations such as pregnant women and children.
Authors: Dwayne L Mann; Philip I Terrill; Ali Azarbarzin; Sara Mariani; Angelo Franciosini; Alessandra Camassa; Thomas Georgeson; Melania Marques; Luigi Taranto-Montemurro; Ludovico Messineo; Susan Redline; Andrew Wellman; Scott A Sands Journal: Eur Respir J Date: 2019-07-04 Impact factor: 16.671
Authors: Dwayne L Mann; Thomas Georgeson; Shane A Landry; Bradley A Edwards; Ali Azarbarzin; Daniel Vena; Lauren B Hess; Andrew Wellman; Susan Redline; Scott A Sands; Philip I Terrill Journal: Sleep Date: 2021-12-10 Impact factor: 6.313