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STDP-based adaptive graph convolutional networks for automatic sleep staging
Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spik...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10157055/ https://www.ncbi.nlm.nih.gov/pubmed/37152593 http://dx.doi.org/10.3389/fnins.2023.1158246 |
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author | Zhao, Yuan Lin, Xianghong Zhang, Zequn Wang, Xiangwen He, Xianrun Yang, Liu |
author_facet | Zhao, Yuan Lin, Xianghong Zhang, Zequn Wang, Xiangwen He, Xianrun Yang, Liu |
author_sort | Zhao, Yuan |
collection | PubMed |
description | Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models. |
format | Online Article Text |
id | pubmed-10157055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101570552023-05-05 STDP-based adaptive graph convolutional networks for automatic sleep staging Zhao, Yuan Lin, Xianghong Zhang, Zequn Wang, Xiangwen He, Xianrun Yang, Liu Front Neurosci Neuroscience Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models. Frontiers Media S.A. 2023-04-20 /pmc/articles/PMC10157055/ /pubmed/37152593 http://dx.doi.org/10.3389/fnins.2023.1158246 Text en Copyright © 2023 Zhao, Lin, Zhang, Wang, He and Yang. 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 Zhao, Yuan Lin, Xianghong Zhang, Zequn Wang, Xiangwen He, Xianrun Yang, Liu STDP-based adaptive graph convolutional networks for automatic sleep staging |
title | STDP-based adaptive graph convolutional networks for automatic sleep staging |
title_full | STDP-based adaptive graph convolutional networks for automatic sleep staging |
title_fullStr | STDP-based adaptive graph convolutional networks for automatic sleep staging |
title_full_unstemmed | STDP-based adaptive graph convolutional networks for automatic sleep staging |
title_short | STDP-based adaptive graph convolutional networks for automatic sleep staging |
title_sort | stdp-based adaptive graph convolutional networks for automatic sleep staging |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10157055/ https://www.ncbi.nlm.nih.gov/pubmed/37152593 http://dx.doi.org/10.3389/fnins.2023.1158246 |
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