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PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs

Traditional supervised learning methods require large quantities of labeled data. However, labeling sleep data according to polysomnography by well-trained sleep experts is a very tedious job. In the present day, the development of self-supervised learning methods is making significant progress in m...

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Detalles Bibliográficos
Autores principales: You, Yuyang, Chang, Shuohua, Yang, Zhihong, Sun, Qihang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10279883/
https://www.ncbi.nlm.nih.gov/pubmed/37346085
http://dx.doi.org/10.3389/fnins.2023.1167723
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author You, Yuyang
Chang, Shuohua
Yang, Zhihong
Sun, Qihang
author_facet You, Yuyang
Chang, Shuohua
Yang, Zhihong
Sun, Qihang
author_sort You, Yuyang
collection PubMed
description Traditional supervised learning methods require large quantities of labeled data. However, labeling sleep data according to polysomnography by well-trained sleep experts is a very tedious job. In the present day, the development of self-supervised learning methods is making significant progress in many fields. It is also possible to apply some of these methods to sleep staging. This is to remove the dependency on labeled data at the stage of representation extraction. Nevertheless, they often rely too much on negative samples for sample selection and construction. Therefore, we propose PSNSleep, a novel self-supervised learning method for sleep staging based on Siamese networks. The crucial step to the success of our method is to select appropriate data augmentations (the time shift block) to construct the positive sample pair. PSNSleep achieves satisfactory results without relying on any negative samples. We evaluate PSNSleep on Sleep-EDF and ISRUC-Sleep and achieve accuracy of 80.0% and 74.4%. The source code is publicly available at https://github.com/arthurxl/PSNSleep.
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spelling pubmed-102798832023-06-21 PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs You, Yuyang Chang, Shuohua Yang, Zhihong Sun, Qihang Front Neurosci Neuroscience Traditional supervised learning methods require large quantities of labeled data. However, labeling sleep data according to polysomnography by well-trained sleep experts is a very tedious job. In the present day, the development of self-supervised learning methods is making significant progress in many fields. It is also possible to apply some of these methods to sleep staging. This is to remove the dependency on labeled data at the stage of representation extraction. Nevertheless, they often rely too much on negative samples for sample selection and construction. Therefore, we propose PSNSleep, a novel self-supervised learning method for sleep staging based on Siamese networks. The crucial step to the success of our method is to select appropriate data augmentations (the time shift block) to construct the positive sample pair. PSNSleep achieves satisfactory results without relying on any negative samples. We evaluate PSNSleep on Sleep-EDF and ISRUC-Sleep and achieve accuracy of 80.0% and 74.4%. The source code is publicly available at https://github.com/arthurxl/PSNSleep. Frontiers Media S.A. 2023-06-06 /pmc/articles/PMC10279883/ /pubmed/37346085 http://dx.doi.org/10.3389/fnins.2023.1167723 Text en Copyright © 2023 You, Chang, Yang and Sun. 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
You, Yuyang
Chang, Shuohua
Yang, Zhihong
Sun, Qihang
PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs
title PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs
title_full PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs
title_fullStr PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs
title_full_unstemmed PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs
title_short PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs
title_sort psnsleep: a self-supervised learning method for sleep staging based on siamese networks with only positive sample pairs
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10279883/
https://www.ncbi.nlm.nih.gov/pubmed/37346085
http://dx.doi.org/10.3389/fnins.2023.1167723
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