Methods developed for automatic sleep stage detection make use of large amounts of data
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …

Automatic sleep stage detection can be performed using a variety of input signals from a
polysomnographic (PSG) recording. In this study, we investigate the effect of different input …

Sleep is vital for one's general well-being, but it is often neglected, which has led to an
increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep …

Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep
stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging …

Background Sleep stage identification is critical in multiple areas (eg medicine or
psychology) to diagnose sleep-related disorders. Previous studies have reported that the …

The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual
procedure requires considerable human and financial resources, and incorporates some …

The analysis and detection of sleep stages continue to preoccupy researchers, particularly
bioinformaticians and neurologists aiming to understand various aspects and functioning of …

Background The rapid advancement in wearable solutions to monitor and score sleep
staging has enabled monitoring outside of the conventional clinical settings. However, most …

The polysomnogram (PSG) analysis is considered the golden standard for sleep staging
under the clinical environment. The electroencephalogram (EEG) signal is the most …

The division of sleep into different stages using EEG signals is a commonplace practice in
sleep laboratories and an indispensable tool for clinicians and researchers. Despite the …