Over the past few decades, researchers have attempted to simplify and accelerate the
process of sleep stage classification through various approaches; however, only a few such …

Sleep staging has a very important role in diagnosing patients with sleep disorders. In
general, this task is very time-consuming for physicians to perform. Deep learning shows …

Despite the progress recently made towards automatic sleep staging for adults, children
have complicated sleep structures that require attention to the pediatric sleep staging. Semi …

Sleep staging is helpful in assessing sleep quality and diagnosing sleep disorders.
However, how to adequately capture the temporal and spatial relations of the brain during …

For real-world automatic sleep-stage classification tasks, various existing deep learning-
based models are biased toward the majority with a high proportion. Because of the unique …

Current methods for sleep stage detection rely on sensors to collect physiological data.
These methods are inaccurate and take up considerable medical resources. Thus, in this …

Sleep staging is a vital process for evaluating sleep quality and diagnosing sleep-related
diseases. Most of the existing automatic sleep staging methods focus on time-domain …

Sleep is crucial for human health. Automatic sleep stage classification based on
polysomnography (PSG) is meaningful for the diagnosis of sleep diseases, which has …

The classification of sleep stages is crucial for gaining insights into an individual's sleep
patterns and identifying potential health issues. Employing several important physiological …

Existing automatic sleep stage classification systems have mostly relied on hand-crafted
features selected from polysomnographic records. To measure the quality of sleep, the …