Discovery of sleep composition types using expectation-maximization

Human sleep exhibits characteristic patterns during the course of a full night, exemplified by alternation of deep sleep (stage N3/4) and light sleep (stages N1 and N2 occasionally interrupted by REM sleep). However, individual variations in this pattern occur. This paper uncovers a coarse classification of such sleep patterns into types described by varying balances among all-night summary variables such as sleep efficiency and the fraction of sleep period time spent in each of the sleep stages N1, N2, N3/4, and REM. Unsupervised expectation-maximization (EM) clustering is used over data obtained from 244 all-night polysomnographic sleep studies, revealing several naturally occurring sleep type clusters. It is found that sleep efficiency plays a major role in differentiating among sleep types, with time in deep sleep further refining the sleep type classification. Associations are found between sleep type on the one hand, and variables that describe health history and habits on the other. These findings suggest that the discovered sleep types describe medically meaningful groups of sleep behaviors that may be useful in future sleep research.