This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep
stage scoring based on raw single-channelEEG. Most of the existing methods rely on hand …

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …

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 …

Deep learning has become popular for automatic sleep stage scoring due to its capability to
extract useful features from raw signals. Most of the existing models, however, have been …

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 …

In recent years, automatic sleep staging methods have achieved competitive performance
using electroencephalography (EEG) signals. However, the acquisition of EEG signals is …

In diagnosing sleep disorders, sleep stage classification is a very essential yet time-
consuming process. Various existing state-of-the-art approaches rely on hand-crafted …

Background and objective In recent years, several automatic sleep stage classification
methods based on convolutional neural networks (CNN) by learning hierarchical feature …

Automatic sleep stage mymargin classification is of great importance to measure sleep
quality. In this paper, we propose a novel attention-based deep learning architecture called …

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 …