The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~15K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting. This data is being used to develop CAISR (Complete AI Sleep Report), a collection of deep neural networks, rule-based algorithms, and signal processing approaches designed to provide better-than-human detection of conventional PSG scoring metrics, including sleep stages, arousals, apnea and hypopnea events and their subtypes, and periodic limb movements. Beyond conventional scoring, the HSP dataset is intended to support research seeking to identify "hidden" information within the brain's activity during sleep that can be used to directly measure brain health. These brain health indicators include measures of risk for common neurologic diseases, including cerebrovascular disease, and Alzheimer's disease and related neurodegenerative diseases of aging; and indicators of response to therapies, including lifestyle interventions (e.g. diet, meditation, exercise) and pharmacologic interventions. These data are shared via the BDSP (Brain Data Science Platform, a resource developed by an international coalition of investigators that aggregates and harmonizes a wide range of large-scale human clinical neuroscience data to support research aimed at improving diagnosis, treatment, and prevention of neurologic disease, and promotion of brain health. The summary data provided here are released for the benefit of the wider scientific community without restriction on use.

The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~19K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting.