{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T13:58:33Z","timestamp":1783605513217,"version":"3.55.0"},"reference-count":80,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Obstructive sleep apnea (OSA) is a commonly underdiagnosed condition that not only increases the risk of accidents but also significantly contributes to a wide range of health complications, including heightened perioperative morbidity and mortality risks during surgeries under general anesthesia. Polysomnography (PSG), which is the diagnostic gold standard, is costly, requires skilled technicians, is time-consuming, and is not always accessible. This study presents a fast, objective, and non-invasive method for detecting OSA severity by analyzing tracheal breathing sounds (TBS) recorded during wakefulness in supine position. Features were extracted from six binary (1-vs-1) severity comparisons\u2014Non-OSA, Mild, Moderate, and Severe\u2014and combined with anthropometric characteristics for classification. The data of 199 subjects (74 Non-OSA, 35 Mild, 50 Moderate, and 40 Severe) were analyzed, the data of 169 and 30 was used for training and blind testing, respectively, and the training dataset was shuffled 10 times to avoid any bias during training. Multiple machine learning models were evaluated, and the best-performing model for each was saved. Across six experimental models comparing OSA severity levels, the most balanced performance was achieved by the Base Model of Non-OSA vs. Severe-OSA using the support vector machine algorithm, with 88.2% accuracy, 83.3% sensitivity, and 90.9% specificity. While Random Forests in the Base Model of Non-OSA vs. Mild-OSA achieved 100% sensitivity, its accuracy was lower (81.2%). The results confirm the reliability and robustness of the proposed approach, providing a basis for OSA severity screening in under 10 min during wakefulness.<\/jats:p>","DOI":"10.3390\/s25206280","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:47:08Z","timestamp":1760104028000},"page":"6280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Assessing Obstructive Sleep Apnea Severity During Wakefulness via Tracheal Breathing Sound Analysis"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5417-0043","authenticated-orcid":false,"given":"Ali","family":"Alqudah","sequence":"first","affiliation":[{"name":"Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9202-949X","authenticated-orcid":false,"given":"Zahra","family":"Moussavi","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"},{"name":"Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"970","DOI":"10.17269\/s41997-024-00911-8","article-title":"Prevalence and Regional Distribution of Obstructive Sleep Apnea in Canada: Analysis from the Canadian Longitudinal Study on Aging","volume":"115","author":"Rizzo","year":"2024","journal-title":"Can. 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