Skip to main contentSkip to search and navigation

UEF eREPOSITORY

    • English
    • suomi
  • English 
    • English
    • suomi
  • Login
View Item 
  •   Home
  • Artikkelit
  • Luonnontieteiden ja metsätieteiden tiedekunta
  • View Item
  •   Home
  • Artikkelit
  • Luonnontieteiden ja metsätieteiden tiedekunta
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Artificial neural network analysis of the oxygen saturation signal enables accurate diagnostics of sleep apnea

Thumbnail
Files
Article (1.056Mb)
Self archived version
published version
Date
2019
Author(s)
Nikkonen, S
Afara, IO
Leppänen, T
Töyräs, J
Unique identifier
10.1038/s41598-019-49330-7
Metadata
Show full item record
More information
Research Database SoleCris

Self-archived article

Citation
Nikkonen, S. Afara, IO. Leppänen, T. Töyräs, J. (2019). Artificial neural network analysis of the oxygen saturation signal enables accurate diagnostics of sleep apnea.  Scientific reports, 9 (1) , 13200. 10.1038/s41598-019-49330-7.
Rights
© The Authors 2019
Licensed under
CC BY http://creativecommons.org/licenses/by/4.0/
Abstract

The severity of obstructive sleep apnea (OSA) is classified using apnea-hypopnea index (AHI). Accurate determination of AHI currently requires manual analysis and complicated registration setup making it expensive and labor intensive. Partially for these reasons, OSA is a heavily underdiagnosed disease as only 7% of women and 18% of men suffering from OSA have diagnosis. To resolve these issues, we introduce an artificial neural network (ANN) that estimates AHI and oxygen desaturation index (ODI) using only the blood oxygen saturation signal (SpO2), recorded during ambulatory polygraphy, as an input. Therefore, hypopneas associated only with an arousal were not considered in this study. SpO2 signals from 1692 patients were used for training and 99 for validation. Two test sets were used consisting of 198 and 1959 patients. In the primary test set, the median absolute errors of ANN estimated AHI and ODI were 0.78 events/hour and 0.68 events/hour respectively. Based on the ANN estimated AHI and ODI, 90.9% and 94.4% of the test patients were classified into the correct OSA severity category. In conclusion, AHI and ODI can be reliably determined using neural network analysis of SpO2 signal. The developed method may enable a more affordable screening of OSA.

An Author Correction to this article was published on 13 March 2020
https://doi.org/10.1038/s41598-020-62003-0

URI
https://erepo.uef.fi/handle/123456789/24539
Link to the original item
http://dx.doi.org/10.1038/s41598-019-49330-7
Publisher
Springer Science and Business Media LLC
Collections
  • Luonnontieteiden ja metsätieteiden tiedekunta [1109]
University of Eastern Finland
OpenAccess
eRepo
erepo@uef.fi
OpenUEF
Service provided by
the University of Eastern Finland Library
Library web pages
Twitter
Facebook
Youtube
Library blog
 sitemap
Search

Browse

All of the ArchiveResource types & CollectionsBy Issue DateAuthorsTitlesSubjectsFacultyDepartmentFull organizationSeriesMain subjectThis CollectionBy Issue DateAuthorsTitlesSubjectsFacultyDepartmentFull organizationSeriesMain subject

My Account

Login
University of Eastern Finland
OpenAccess
eRepo
erepo@uef.fi
OpenUEF
Service provided by
the University of Eastern Finland Library
Library web pages
Twitter
Facebook
Youtube
Library blog
 sitemap