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EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal
In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8311494/ https://www.ncbi.nlm.nih.gov/pubmed/34321991 http://dx.doi.org/10.3389/fnins.2021.573194 |
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author | Fan, Jiahao Sun, Chenglu Long, Meng Chen, Chen Chen, Wei |
author_facet | Fan, Jiahao Sun, Chenglu Long, Meng Chen, Chen Chen, Wei |
author_sort | Fan, Jiahao |
collection | PubMed |
description | In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG. A two-scale convolutional neural network first extracts epoch-wise temporary-equivalent features from raw EOG signals. A recurrent neural network then captures the long-term sequential information. The proposed method was validated on 101 full-night sleep data from two open-access databases, the montreal archive of sleep studies and Sleep-EDF, achieving an overall accuracy of 81.2 and 76.3%, respectively. The results are comparable to those models trained with EEG signals. In addition, comparisons with six state-of-the-art methods further demonstrate the effectiveness of the proposed approach. Overall, this study provides a new avenue for sleep monitoring. |
format | Online Article Text |
id | pubmed-8311494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83114942021-07-27 EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal Fan, Jiahao Sun, Chenglu Long, Meng Chen, Chen Chen, Wei Front Neurosci Neuroscience In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals. However, the acquisition of EEG signals is cumbersome and inconvenient. Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG. A two-scale convolutional neural network first extracts epoch-wise temporary-equivalent features from raw EOG signals. A recurrent neural network then captures the long-term sequential information. The proposed method was validated on 101 full-night sleep data from two open-access databases, the montreal archive of sleep studies and Sleep-EDF, achieving an overall accuracy of 81.2 and 76.3%, respectively. The results are comparable to those models trained with EEG signals. In addition, comparisons with six state-of-the-art methods further demonstrate the effectiveness of the proposed approach. Overall, this study provides a new avenue for sleep monitoring. Frontiers Media S.A. 2021-07-12 /pmc/articles/PMC8311494/ /pubmed/34321991 http://dx.doi.org/10.3389/fnins.2021.573194 Text en Copyright © 2021 Fan, Sun, Long, Chen and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Fan, Jiahao Sun, Chenglu Long, Meng Chen, Chen Chen, Wei EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal |
title | EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal |
title_full | EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal |
title_fullStr | EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal |
title_full_unstemmed | EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal |
title_short | EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal |
title_sort | eognet: a novel deep learning model for sleep stage classification based on single-channel eog signal |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8311494/ https://www.ncbi.nlm.nih.gov/pubmed/34321991 http://dx.doi.org/10.3389/fnins.2021.573194 |
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