{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T22:19:59Z","timestamp":1790806799429,"version":"4.1.0"},"reference-count":40,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sleep Research"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolence, is challenging due to overlapping symptoms with other hypersomnolence disorders. While recent years have seen significant growth in our understanding of nocturnal polysomnography narcolepsy type 1 features, there remains a need for improving methods to differentiate narcolepsy type 1 nighttime sleep features from those of individuals without narcolepsy type 1. We aimed to develop a machine learning framework for identifying sleep features to discriminate narcolepsy type 1 from clinical controls, narcolepsy type 2 and idiopathic hypersomnia. The population included polysomnography data from 350 drug\u2010free individuals (114 narcolepsy type 1, 90 narcolepsy type 2, 105 idiopathic hypersomnia, and 41 clinical controls) collected at the National Reference Centers for Narcolepsy in Montpelier, France. Several sets of nocturnal sleep features were explored, as well as the value of time\u2010resolving sleep architecture by analysing sleep per quarter\u2010night. Several patterns of nighttime sleep evolution emerged that differed between narcolepsy type 1, clinical controls, narcolepsy type 2 and idiopathic hypersomnia, with increased nighttime instability observed in patients with narcolepsy type 1. Using machine learning models, we identified rapid eye movement sleep onset as the best single polysomnography feature to distinguish narcolepsy type 1 from controls, narcolepsy type 2 and idiopathic hypersomnia. By combining multiple feature sets capturing different aspects of sleep across quarter\u2010night periods, we were able to further improve between\u2010group discrimination and could identify the most discriminative sleep features. Our results highlight salient polysomnography features and the relevance of assessing their time\u2010dependent changes during sleep that could aid diagnosis and measure the impact of novel therapeutics in future clinical trials.<\/jats:p>","DOI":"10.1111\/jsr.14216","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T03:33:33Z","timestamp":1714102413000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Identifying time\u2010resolved features of nocturnal sleep characteristics of narcolepsy using machine learning"],"prefix":"10.1111","volume":"33","author":[{"given":"Marco","family":"Vilela","sequence":"first","affiliation":[{"name":"Takeda Development Center Americas, Inc.  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1526-6349","authenticated-orcid":false,"given":"Brian","family":"Tracey","sequence":"additional","affiliation":[{"name":"Takeda Development Center Americas, Inc.  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitri","family":"Volfson","sequence":"additional","affiliation":[{"name":"Takeda Development Center Americas, Inc.  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