Nature communications · 2018
Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Stephansen Jens B, Olesen Alexander N, Olsen Mads, Ambati Aditya, Leary Eileen B, Moore Hyatt E, Carrillo Oscar, Lin Ling, Han Fang, Yan Han, Sun Yun L, Dauvilliers Yves, Scholz Sabine, Barateau Lucie, Hogl Birgit, Stefani Ambra, Hong Seung Chul, Kim Tae Won, Pizza Fabio, Plazzi Giuseppe, Vandi Stefano, Antelmi Elena, Perrin Dimitri, Kuna Samuel T, Schweitzer Paula K, Kushida Clete, Peppard Paul E, Sorensen Helge B D, Jennum Poul, Mignot Emmanuel
Аннотация
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.