This paper proposes a practical approach to addressing limitations posed by using of single-
channel electroencephalography (EEG) for sleep stage classification. EEG-based …

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the
patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult …

Sleep stage scoring is a challenging task. Most of existing sleep stage classification
approaches rely on analysing electroencephalography (EEG) signals in time or frequency …

Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in
neurological and clinical studies. NCP represents the mental/cognitive human capacity in …

Sleep stage classification is one of the most critical steps in effective diagnosis and the
treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time …

We propose in this work a feature learning approach using deep bidirectional recurrent
neural networks (RNNs) with attention mechanism for single-channel automatic sleep stage …

Background Sleep staging is a critical step in a range of electrophysiological signal
processing pipelines used in clinical routine as well as in sleep research. Although the …

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 …

Automated classification of sleep stages is in demand to overcome the limitations of manual
sleep stage classification. Analyzing sleep stages manually using neurophysiological …

This paper proposes a classification framework for automatic sleep stage detection in both
male and female human subjects by analyzing the electroencephalogram (EEG) data of …