Extensive research has been conducted on the automatic classification of sleep stages
utilizing deep neural networks and other neurophysiological markers. However, for sleep …

Automatic sleep stage classification of multi-channel sleep signals can help clinicians
efficiently evaluate an individual's sleep quality and assist in diagnosing a possible sleep …

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of
skilled clinicians. Deep learning approaches have been introduced in order to challenge the …

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders.
This paper proposes a joint classification-and-prediction framework based on convolutional …

Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health
issues from human sleep data. Manual sleep staging requires human expertise, which is …

Sleep stage classification is crucial in diagnosing sleep disorders and monitoring treatment
effectiveness, yet it is inconvenient, requiring many electrodes and labor-intensive …

Sleep stage classification plays a significant role in the accurate diagnosis and treatment of
sleep-related diseases. This study aims to develop an efficient deep learning based scheme …

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 …

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 …

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 …