Sleep is a fascinating field of research with implications for a wide range of disciplines, from
neuroscience to psychology and beyond. It is well known that good sleep is essential for …

The classification of sleep stages is a pivotal aspect of diagnosing sleep disorders and
evaluating sleep quality. However, the conventional manual scoring process, conducted by …

Sleep stage classification based on EEG is a crucial tool for understanding sleep quality. It
enables the identification of various stages of sleep, including REM and non-REM sleep …

Automatic sleep stage scoring based on deep neural networks has come into focus of sleep
researchers and physicians, as a reliable method able to objectively classify sleep stages …

Introduction: We propose an automatic sleep stage scoring model, referred to as
SeriesSleepNet, based on convolutional neural network (CNN) and bidirectional long short …

Sleep stage classification constitutes an important preliminary exam in the diagnosis of
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …

Abstract Background and Objective Sleep staging is an essential step for sleep disorder
diagnosis, which is time-intensive and laborious for experts to perform this work manually …

Sleep, as the basis for regular body functioning, can affect human health. Poor sleep
conditions can lead to various physical ailments, such as poor immunity, memory loss, slow …

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep
stage scoring based on raw single-channelEEG. Most of the existing methods rely on hand …

Automatic sleep scoring is essential for the diagnosis and treatment of sleep disorders and
enables longitudinal sleep tracking in home environments. Conventionally, learning-based …