DeepAI AI Chat
Log In Sign Up

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

by   Sajad Mousavi, et al.
Northern Arizona University
Ngee Ann Polytechnic

Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26 Cohen's Kappa coefficient = 0.79. Our developed model is ready to test with more sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at


page 3

page 4

page 8


DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

The present study proposes a deep learning model, named DeepSleepNet, fo...

Automatic detection of microsleep episodes with deep learning

Brief fragments of sleep shorter than 15 s are defined as microsleep epi...

Crude EEG parameter provides sleep medicine with well-defined continuous hypnograms

To evaluate EEG data, one can count local maxima and minima on a fine sc...

Application of Machine Learning to Sleep Stage Classification

Sleep studies are imperative to recapitulate phenotypes associated with ...

Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal

Sleeping problems have become one of the major diseases all over the wor...

Code Repositories


SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

view repo