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A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG
Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953225/ https://www.ncbi.nlm.nih.gov/pubmed/36830864 http://dx.doi.org/10.3390/biomedicines11020327 |
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author | You, Yuyang Guo, Xiaoyu Yang, Zhihong Shan, Wenjing |
author_facet | You, Yuyang Guo, Xiaoyu Yang, Zhihong Shan, Wenjing |
author_sort | You, Yuyang |
collection | PubMed |
description | Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based on Siamese networks, based on single-channel EEG. Our proposed method consists of a Siamese network architecture and a redesigned loss with distance metrics. Two encoders are used in the Siamese network to generate latent features of the EEG epochs, and the contrastive loss, which is also a distance metric, is used to compare the similarity or differences between EEG epochs from the same or different sleep stages. We evaluated our method on single-channel EEGs from different channels (Fpz-Cz and F4-EOG (left)) from two public datasets SleepEDF and MASS-SS3 and achieved the overall accuracies MF1 and Cohen’s kappa coefficient of 85.2%, 78.3% and 0.79 on SleepEDF and 87.2%, 82.1% and 0.81 on MASS-SS3. The results show that our method can significantly improve the performance of sleep staging models and outperform the state-of-the-art sleep staging methods. The performance of our method also confirms that the features captured by Siamese networks and distance metrics are useful for sleep staging. |
format | Online Article Text |
id | pubmed-9953225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99532252023-02-25 A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG You, Yuyang Guo, Xiaoyu Yang, Zhihong Shan, Wenjing Biomedicines Article Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based on Siamese networks, based on single-channel EEG. Our proposed method consists of a Siamese network architecture and a redesigned loss with distance metrics. Two encoders are used in the Siamese network to generate latent features of the EEG epochs, and the contrastive loss, which is also a distance metric, is used to compare the similarity or differences between EEG epochs from the same or different sleep stages. We evaluated our method on single-channel EEGs from different channels (Fpz-Cz and F4-EOG (left)) from two public datasets SleepEDF and MASS-SS3 and achieved the overall accuracies MF1 and Cohen’s kappa coefficient of 85.2%, 78.3% and 0.79 on SleepEDF and 87.2%, 82.1% and 0.81 on MASS-SS3. The results show that our method can significantly improve the performance of sleep staging models and outperform the state-of-the-art sleep staging methods. The performance of our method also confirms that the features captured by Siamese networks and distance metrics are useful for sleep staging. MDPI 2023-01-24 /pmc/articles/PMC9953225/ /pubmed/36830864 http://dx.doi.org/10.3390/biomedicines11020327 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article You, Yuyang Guo, Xiaoyu Yang, Zhihong Shan, Wenjing A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG |
title | A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG |
title_full | A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG |
title_fullStr | A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG |
title_full_unstemmed | A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG |
title_short | A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG |
title_sort | siamese network-based method for improving the performance of sleep staging with single-channel eeg |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953225/ https://www.ncbi.nlm.nih.gov/pubmed/36830864 http://dx.doi.org/10.3390/biomedicines11020327 |
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