Back to Search Start Over

NAMRTNet: Automatic Classification of Sleep Stages Based on Improved ResNet-TCN Network and Attention Mechanism

Authors :
Xuebin Xu
Chen Chen
Kan Meng
Longbin Lu
Xiaorui Cheng
Haichao Fan
Source :
Applied Sciences, Vol 13, Iss 11, p 6788 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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 cognitive development, and cardiovascular diseases. Along the increasing stress in society comes with a growing surge in conditions associated with sleep disorders. Studies have shown that sleep stages are essential for the body’s memory, immune system, and brain functioning. Therefore, automatic sleep stage classification is of great medical practice importance as a basis for monitoring sleep conditions. Although previous research into the classification of sleep stages has been promising, several challenges remain to be addressed: (1) The EEG signal is a non-smooth signal with harrowing feature extraction and high requirements for model accuracy. (2) Some existing network models suffer from overfitting and gradient descent. (3) Correlation between long time sequences is challenging to capture. This paper proposes NAMRTNet, a deep model architecture based on the original single-channel EEG signal to address these challenges. The model uses a modified ResNet network to extract features from sub-epochs of individual epochs, a lightweight attention mechanism normalization-based attention module (NAM) to suppress insignificant features, and a temporal convolutional network (TCN) network to capture dependencies between features of long time series. The recognition rate of 20-fold cross-validation with the NAMRTNet model for Fpz-cz channel data in the public sleep dataset Sleep-EDF was 86.2%. The experimental results demonstrate the network’s superiority in this paper, surpassing some state-of-the-art techniques in different evaluation metrics. Furthermore, the total time to train the network was 5.1 h, which was much less than the training time of other models.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.95f3090b923f4ae3848db301b13f27c3
Document Type :
article
Full Text :
https://doi.org/10.3390/app13116788