The paper of Stephansen et al. in Nature Communications was chosen as the paper of the month of December. In this multicenter study, automated sleep scoring was performed in approximately 3000 normal and abnormal sleep recordings. A deep learning neural network analysis technique was used to produce per subject hypnodensity graphs with 5 s epochs that included the probability of occurrence of each sleep stage. The researchers validated the technique by comparing the automated scoring of 70 patients with narcolepsy type 1 with the scores of six manual raters. The best learning model performed better than any individual scorer (87% versus consensus). The researchers suggest that the method could reduce time spent in sleep clinics and even automate narcolepsy type 1 diagnosis.

The article, entitled: “Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy” (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) is available here.