People vary in both the quantity and the quality of their sleep. Traditional sleep ?stages? (for example, slow- wave sleep or REM sleep) can exhibit different neural correlates (as measured by electroencephalography, EEG) in different people and at different times during the night. Standard approaches for characterizing sleep (?staging?) do not take this variability into account, however. Here we suggest that attempts to use EEG and other data to infer sleep stages should in fact tackle the heterogeneity between and within individuals explicitly, to yield more accurate classification not just of an individual?s minute-by-minute sleep, but of the sleeper?s typical characteristics more generally. Quantitative, data-driven individual sleep profiles will be important prerequisites for emerging large-scale molecular genetics and other ?omics studies of sleep, and for driving personalized sleep medicine more generally. Specifically, we propose to leverage more than ten thousand whole-night polysomnography (PSG) studies from the demographically diverse National Sleep Research Resource (NSRR), in order to optimally apply machine-learning methods to sleep signals. Most importantly, the NSRR dataset will enable approaches that take the fundamental heterogeneity of sleep into account: this is a rate-limiting step that has not been tackled by previous approaches. We hypothesize that it is not the large training set per se, but rather the increased prospects for better-matched training data, combined with the methods developed here, that will be beneficial. To this end, we will build and disseminate a novel, multi-level automated sleep classifier based on the world?s largest dataset of sleep signals.
People vary considerably not just in how much they sleep, but also in the types of sleep they typically have. Standard approaches to describing sleep, primarily developed in the 1960s, only partially capture this variability: we propose to use a range of measures, including brain activity, on over ten thousand sleeping individuals to generate more accurate, data-driven, personalized profiles of how a person's sleep varies over a night. Sleep factors are associated with multiple physical and neuropsychiatric illnesses, and so better measurement of the underlying processes will be critical for future genetic and biomarker studies aimed at understanding the links between sleep, health and disease.
