Résumé
Nature Communications volume 9, Article number: 5229 (2018) 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 instead of 30 second 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.