{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-network-an1alysis-of-sleep-stages","title":"Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy","arxiv_id":"1710.02094","date":"2017-10-05","proceeding":null,"authors":["Jens B. Stephansen","Alexander N. Olesen","Mads Olsen","Aditya Ambati","Eileen B. Leary","Hyatt E. Moore","Oscar Carrillo","Ling Lin","Fang Han","Han Yan","Yun L. Sun","Yves Dauvilliers","Sabine Scholz","Lucie Barateau","Birgit Hogl","Ambra Stefani","Seung Chul Hong","Tae Won Kim","Fabio Pizza","Giuseppe Plazzi","Stefano Vandi","Elena Antelmi","Dimitri Perrin","Samuel T. Kuna","Paula K. Schweitzer","Clete Kushida","Paul E. Peppard","Helge B. D. Sorensen","Poul Jennum","Emmanuel Mignot"],"abstract":"Analysis of sleep for the diagnosis of sleep disorders such as Type-1\nNarcolepsy (T1N) currently requires visual inspection of polysomnography\nrecords by trained scoring technicians. Here, we used neural networks in\napproximately 3,000 normal and abnormal sleep recordings to automate sleep\nstage scoring, producing a hypnodensity graph - a probability distribution\nconveying more information than classical hypnograms. Accuracy of sleep stage\nscoring was validated in 70 subjects assessed by six scorers. The best model\nperformed better than any individual scorer (87% versus consensus). It also\nreliably scores sleep down to 5 instead of 30 second scoring epochs. A T1N\nmarker based on unusual sleep-stage overlaps achieved a specificity of 96% and\na sensitivity of 91%, validated in independent datasets. Addition of\nHLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time\nspent in sleep clinics and automates T1N diagnosis. It also opens the\npossibility of diagnosing T1N using home sleep studies.","url_abs":"http://arxiv.org/abs/1710.02094v2","url_pdf":"http://arxiv.org/pdf/1710.02094v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-network-an1alysis-of-sleep-stages","repo_url":"https://github.com/stanford-stages/stanford-stages","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1710.02094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.02094"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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