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 …

Automatic sleep staging has been often treated as a simple classification problem that aims
at determining the label of individual target polysomnography epochs one at a time. In this …

Sleep stage classification is a most important process in sleep scoring which is used to
evaluate sleep quality and diagnose sleep-related diseases. Compared to complex sleep …

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 …

In recent years, automatic sleep staging methods have achieved competitive performance
using electroencephalography (EEG) signals. However, the acquisition of EEG signals is …

Sleep is a vital process of our daily life as we roughly spend one-third of our lives asleep. In
order to evaluate sleep quality and potential sleep disorders, sleep stage classification is a …

Deep Learning (DL) has been recently employed to build smart systems that perform
incredibly well in a wide range of tasks, such as image recognition, machine translation, and …

Automatic sleep staging methods usually extract hand-crafted features or network trained
features from signals recorded by polysomnography (PSG), and then estimate the stages by …

Abstract Objective Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour
Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully …

Objective. In this study, we conducted a comparative analysis of deep convolutional neural
network (CNN) models in predicting obstructive sleep apnea (OSA) using …