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Development of a human-computer collaborative sleep scoring system for polysomnography recordings
The overnight polysomnographic (PSG) recordings of patients were scored by an expert to diagnose sleep disorders. Visual sleep scoring is a time-consuming and subjective process. Automatic sleep staging methods can help; however, the mechanism and reliability of these methods are not fully understoo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6619661/ https://www.ncbi.nlm.nih.gov/pubmed/31291270 http://dx.doi.org/10.1371/journal.pone.0218948 |
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author | Liang, Sheng-Fu Shih, Yu-Hsuan Chen, Peng-Yu Kuo, Chih-En |
author_facet | Liang, Sheng-Fu Shih, Yu-Hsuan Chen, Peng-Yu Kuo, Chih-En |
author_sort | Liang, Sheng-Fu |
collection | PubMed |
description | The overnight polysomnographic (PSG) recordings of patients were scored by an expert to diagnose sleep disorders. Visual sleep scoring is a time-consuming and subjective process. Automatic sleep staging methods can help; however, the mechanism and reliability of these methods are not fully understood. Therefore, experts often need to rescore the recordings to obtain reliable results. Here, we propose a human-computer collaborative sleep scoring system. It is a rule-based automatic sleep scoring method that follows the American Academy of Sleep Medicine (AASM) guidelines to perform an initial scoring. Then, the reliability level of each epoch is analyzed based on physiological patterns during sleep and the characteristics of various stage changes. Finally, experts would only need to rescore epochs with a low-reliability level. The experimental results show that the average agreement rate between our system and fully manual scorings can reach 90.42% with a kappa coefficient of 0.85. Over 50% of the manual scoring time can be reduced. Due to the demonstrated robustness and applicability, the proposed approach can be integrated with various PSG systems or automatic sleep scoring methods for sleep monitoring in clinical or homecare applications in the future. |
format | Online Article Text |
id | pubmed-6619661 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-66196612019-07-25 Development of a human-computer collaborative sleep scoring system for polysomnography recordings Liang, Sheng-Fu Shih, Yu-Hsuan Chen, Peng-Yu Kuo, Chih-En PLoS One Research Article The overnight polysomnographic (PSG) recordings of patients were scored by an expert to diagnose sleep disorders. Visual sleep scoring is a time-consuming and subjective process. Automatic sleep staging methods can help; however, the mechanism and reliability of these methods are not fully understood. Therefore, experts often need to rescore the recordings to obtain reliable results. Here, we propose a human-computer collaborative sleep scoring system. It is a rule-based automatic sleep scoring method that follows the American Academy of Sleep Medicine (AASM) guidelines to perform an initial scoring. Then, the reliability level of each epoch is analyzed based on physiological patterns during sleep and the characteristics of various stage changes. Finally, experts would only need to rescore epochs with a low-reliability level. The experimental results show that the average agreement rate between our system and fully manual scorings can reach 90.42% with a kappa coefficient of 0.85. Over 50% of the manual scoring time can be reduced. Due to the demonstrated robustness and applicability, the proposed approach can be integrated with various PSG systems or automatic sleep scoring methods for sleep monitoring in clinical or homecare applications in the future. Public Library of Science 2019-07-10 /pmc/articles/PMC6619661/ /pubmed/31291270 http://dx.doi.org/10.1371/journal.pone.0218948 Text en © 2019 Liang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Liang, Sheng-Fu Shih, Yu-Hsuan Chen, Peng-Yu Kuo, Chih-En Development of a human-computer collaborative sleep scoring system for polysomnography recordings |
title | Development of a human-computer collaborative sleep scoring system for polysomnography recordings |
title_full | Development of a human-computer collaborative sleep scoring system for polysomnography recordings |
title_fullStr | Development of a human-computer collaborative sleep scoring system for polysomnography recordings |
title_full_unstemmed | Development of a human-computer collaborative sleep scoring system for polysomnography recordings |
title_short | Development of a human-computer collaborative sleep scoring system for polysomnography recordings |
title_sort | development of a human-computer collaborative sleep scoring system for polysomnography recordings |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6619661/ https://www.ncbi.nlm.nih.gov/pubmed/31291270 http://dx.doi.org/10.1371/journal.pone.0218948 |
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