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.

S and full research articles published in SLEEP, the official journal of the APSS. Sleep and sleep disorder scientific findings.

Read the full review for this F1000Prime recommended article: The association between obstructive sleep apnea and neurocognitive performance--the Apnea Positive Pressure Long-term Efficacy Study (APPLES).

Sleep issues affect approximately 50 to 70 million individuals in the US,¹ contributing to multisystemic medical consequences including cardiovascular,^2,3 metabolic,⁴ psychiatric,^5,6 impaired cognition,^7,8 quality of life,⁹ and all-cause mortality.² Numerous studies have reported on the association between sleep and mortality, most focusing on effects of self-reported sleep duration.^3,10,11 Despite emerging evidence of a sleep-mortality association, determining the aspects of sleep driving the association has been a challenge given the multidimensionality of sleep.