This work proposed a novel method for automatic sleep stage classification based on the
time, frequency, and fractional Fourier transform (FRFT) domain features extracted from a …

Although convolutional neural network (CNN) has become very popular, and has been
applied to the sleep stage classification problem, almost all existing studies on sleep stage …

Background and objective: This paper presents a new framework for automatic classification
of sleep stages using a deep learning algorithm from single-channel EEG signals. Each …

For automatic sleep stage classification, the existing methods mostly rely on hand-crafted
features selected from polysomnographic records. In this paper, the goal is to develop a …

Sleep stage scoring is fundamental for the examination and analysis of sleep problems.
Sleep experts score sleep by analyzing brain activity, muscle activity, and eye activity …

Objective. Computerized classification of sleep stages based on single-lead
electroencephalography (EEG) signals is important, but still challenging. In this paper, we …

Sleep disorder is a symptom of many neurological diseases that may significantly affect the
quality of daily life. Traditional methods are time-consuming and involve the manual scoring …

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis
and treatment of sleep disorders, and its automation has been an area of active study. The …

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 …

Background and objective Despite numerous deep learning methods being developed for
automatic sleep stage classification, almost all the models need labeled data. However …