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 …

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

Using a smart method for automatic diagnosis in medical applications, such as sleep stage
classification is considered as one of the important challenges of the last few years which …

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 …

For automatic sleep stage classification, the existing methods mostly rely on hand-crafted
features selected from polysomnographic records. In this paper, the goal is to develop a …

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 …

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 …

We present in this paper an efficient convolutional neural network (CNN) running on time-
frequency image features for automatic sleep stage classification. Opposing to deep …

Sleep stage classification is an essential process for analyzing sleep and diagnosing sleep
related disorders. Sleep staging by visual inspection of expert is a labor-intensive task and …

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