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Auto-annotating sleep stages based on polysomnographic data
Sleep disorders affect the quality of life, and the clinical diagnosis of sleep disorders is a time-consuming and tedious process requiring recording and annotating polysomnographic records. In this work, we developed an auto-annotation algorithm based on polysomnographic records and a deep learning...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767308/ https://www.ncbi.nlm.nih.gov/pubmed/35079710 http://dx.doi.org/10.1016/j.patter.2021.100371 |
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author | Zhang, Hanrui Wang, Xueqing Li, Hongyang Mehendale, Soham Guan, Yuanfang |
author_facet | Zhang, Hanrui Wang, Xueqing Li, Hongyang Mehendale, Soham Guan, Yuanfang |
author_sort | Zhang, Hanrui |
collection | PubMed |
description | Sleep disorders affect the quality of life, and the clinical diagnosis of sleep disorders is a time-consuming and tedious process requiring recording and annotating polysomnographic records. In this work, we developed an auto-annotation algorithm based on polysomnographic records and a deep learning architecture that predicts sleep stages at the millisecond level. The model improves the efficiency of the polysomnographic record annotation process by automatically annotating each record within 3.8 s of computation time and with high accuracy. Disease-related sleep stages, such as arousal and apnea, can also be identified by this model, which further expands the physiological insights that the model can potentially provide. Finally, we explored the applicability of the model to data collected from a different modality to demonstrate the robustness of the model. |
format | Online Article Text |
id | pubmed-8767308 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-87673082022-01-24 Auto-annotating sleep stages based on polysomnographic data Zhang, Hanrui Wang, Xueqing Li, Hongyang Mehendale, Soham Guan, Yuanfang Patterns (N Y) Article Sleep disorders affect the quality of life, and the clinical diagnosis of sleep disorders is a time-consuming and tedious process requiring recording and annotating polysomnographic records. In this work, we developed an auto-annotation algorithm based on polysomnographic records and a deep learning architecture that predicts sleep stages at the millisecond level. The model improves the efficiency of the polysomnographic record annotation process by automatically annotating each record within 3.8 s of computation time and with high accuracy. Disease-related sleep stages, such as arousal and apnea, can also be identified by this model, which further expands the physiological insights that the model can potentially provide. Finally, we explored the applicability of the model to data collected from a different modality to demonstrate the robustness of the model. Elsevier 2021-10-28 /pmc/articles/PMC8767308/ /pubmed/35079710 http://dx.doi.org/10.1016/j.patter.2021.100371 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Zhang, Hanrui Wang, Xueqing Li, Hongyang Mehendale, Soham Guan, Yuanfang Auto-annotating sleep stages based on polysomnographic data |
title | Auto-annotating sleep stages based on polysomnographic data |
title_full | Auto-annotating sleep stages based on polysomnographic data |
title_fullStr | Auto-annotating sleep stages based on polysomnographic data |
title_full_unstemmed | Auto-annotating sleep stages based on polysomnographic data |
title_short | Auto-annotating sleep stages based on polysomnographic data |
title_sort | auto-annotating sleep stages based on polysomnographic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767308/ https://www.ncbi.nlm.nih.gov/pubmed/35079710 http://dx.doi.org/10.1016/j.patter.2021.100371 |
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