{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:43:45Z","timestamp":1765233825680,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Respiratory effort is considered important in the context of the diagnosis of obstructive sleep apnoea (OSA), as well as other sleep disorders. However, current monitoring techniques can be obtrusive and interfere with a patient\u2019s natural sleep. This study examines the reliability of an unobtrusive tracheal sound-based approach to monitor respiratory effort in the context of OSA, using manually marked respiratory inductance plethysmography (RIP) signals as a gold standard for validation. Methods: In total, 150 patients were trained on the use of type III cardiorespiratory polygraphy, which they took to use at home, alongside a neck-worn AcuPebble system. The respiratory effort channels obtained from the tracheal sound recordings were compared to the effort measured by the RIP bands during automatic and manual marking experiments. A total of 133 central apnoeas, 218 obstructive apnoeas, 263 obstructive hypopneas, and 270 normal breathing randomly selected segments were shuffled and blindly marked by a Registered Polysomnographic Technologist (RPSGT) in both types of channels. The RIP signals had previously also been independently marked by another expert clinician in the context of diagnosing those patients, and without access to the effort channel of AcuPebble. The classification achieved with the acoustically obtained effort was assessed with statistical metrics and the average amplitude distributions per respiratory event type for each of the different channels were also studied to assess the overlap between event types. Results: The performance of the acoustic effort channel was evaluated for the events where both scorers were in agreement in the marking of the gold standard reference channel, showing an average sensitivity of 90.5%, a specificity of 98.6%, and an accuracy of 96.8% against the reference standard with blind expert marking. In addition, a comparison using the Embla Remlogic 4.0 automatic software of the reference standard for classification, as opposed to the expert marking, showed that the acoustic channels outperformed the RIP channels (acoustic sensitivity: 71.9%; acoustic specificity: 97.2%; RIP sensitivity: 70.1%; RIP specificity: 76.1%). The amplitude trends across different event types also showed that the acoustic channels exhibited a better differentiation between the amplitude distributions of different event types, which can help when doing manual interpretation. Conclusions: The results prove that the acoustically obtained effort channel extracted using AcuPebble is an accurate, reliable, and more patient-friendly alternative to RIP in the context of OSA.<\/jats:p>","DOI":"10.3390\/jcm13123628","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T11:42:03Z","timestamp":1718883723000},"page":"3628","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Validation of Tracheal Sound-Based Respiratory Effort Monitoring for Obstructive Sleep Apnoea Diagnosis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0207-8609","authenticated-orcid":false,"given":"Mireia","family":"Mu\u00f1oz Rojo","sequence":"first","affiliation":[{"name":"Acurable, London SW1H 0NB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renard Xaviero Adhi","family":"Pramono","sequence":"additional","affiliation":[{"name":"Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College of Science Technology and Medicine, London SW7 2BX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikesh","family":"Devani","sequence":"additional","affiliation":[{"name":"Thoracic Medicine, Royal Free London NHS Foundation Trust, London NW3 2QG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Thomas","sequence":"additional","affiliation":[{"name":"Acurable, London SW1H 0NB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2232-5880","authenticated-orcid":false,"given":"Swapna","family":"Mandal","sequence":"additional","affiliation":[{"name":"Thoracic Medicine, Royal Free London NHS Foundation Trust, London NW3 2QG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esther","family":"Rodriguez-Villegas","sequence":"additional","affiliation":[{"name":"Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College of Science Technology and Medicine, London SW7 2BX, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"359","DOI":"10.5664\/jcsm.6978","article-title":"Characterization of Respiratory Events in Obstructive Sleep Apnea Using Suprasternal Pressure Monitoring","volume":"14","author":"Glos","year":"2018","journal-title":"J. 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