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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...

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Autores principales: Zhao, Yuan, Lin, Xianghong, Zhang, Zequn, Wang, Xiangwen, He, Xianrun, Yang, Liu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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.
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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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