Modern deep learning holds a great potential to transform clinical studies of human sleep.
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …

The field of sleep is in many ways ideally positioned to take full advantage of advancements
in technology and analytics that is fueling the mobile health movement. Combining …

Sleep disorders affect a large portion of the global population and are strong predictors of
morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence …

The clinical and societal measurement of human sleep has increased exponentially in
recent years. However, unlike other fields of medical analysis that have become highly …

Background: Black-box skepticism is one of the main hindrances impeding deep-learning-
based automatic sleep scoring from being used in clinical environments. Methods: Towards …

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve
millions experiencing sleep deprivation and disorders and enable longitudinal sleep …

Abstract Study Objectives The development of ambulatory technologies capable of
monitoring brain activity during sleep longitudinally is critical for advancing sleep science …

Objectives Scoring laboratory polysomnography (PSG) data remains a manual task of
visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb …

The identification of sleep stages is essential in the diagnostics of sleep disorders, among
which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring …

Background: Despite recent significant progress in the development of automatic sleep
staging methods, building a good model still remains a big challenge for sleep studies with a …