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 …

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 …

Increasingly prevalent sleep disorders worldwide significantly affect the well-being of
individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence …

Recently, the significance of accurate aircraft delay forecasting has grown in the aviation
sector, which caused multi-billion-dollar losses faced by airlines and airports and passenger …

Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …

Manual scoring of sleep stages from polysomnography (PSG) records is essential to
understand the sleep quality and architecture. Since the PSG requires specialized …

The analysis of sleep stages for children plays an important role in early diagnosis and
treatment. This paper introduces our sleep stage classification method addressing the …

Sleep is an important research area in nutritional medicine that plays a crucial role in human
physical and mental health restoration. It can influence diet, metabolism, and hormone …

Sleep staging is an indispensable indicator for measuring sleep quality and evaluating
sleep disorders. Deep learning methods have been successfully applied to automatic sleep …

Building machine learning models using EEG recorded outside of the laboratory setting
requires methods robust to noisy data and randomly missing channels. This need is …