Automatic sleep stage scoring based on deep neural networks has come into focus of sleep
researchers and physicians, as a reliable method able to objectively classify sleep stages …

Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive
and physical well-being and pathological conditions. A prerequisite for further analysis is the …

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 …

The classification of sleep stages is the first and an important step in the quantitative
analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual …

We present a novel method for automatic sleep scoring based on single-channel EEG. We
introduce the use of a deep convolutional neural network (CNN) on raw EEG samples for …

Sleep, as the basis for regular body functioning, can affect human health. Poor sleep
conditions can lead to various physical ailments, such as poor immunity, memory loss, slow …

Analysis of sleep stages using electroencephalograms (EEGs), a critical procedure in health
monitoring, has been researched extensively. Scoring of the sleep stages is highly …

Convolutional neural networks (CNN) have demonstrated state-of-the-art classification
results in image categorization, but have received comparatively little attention for …

Effective sleep analysis is hampered by the lack of automated tools catering to disordered
sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning …

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