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An open-source, high-performance tool for automated sleep staging

The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consumin...

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Detalles Bibliográficos
Autores principales: Vallat, Raphael, Walker, Matthew P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516415/
https://www.ncbi.nlm.nih.gov/pubmed/34648426
http://dx.doi.org/10.7554/eLife.70092
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author Vallat, Raphael
Walker, Matthew P
author_facet Vallat, Raphael
Walker, Matthew P
author_sort Vallat, Raphael
collection PubMed
description The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package.
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spelling pubmed-85164152021-10-15 An open-source, high-performance tool for automated sleep staging Vallat, Raphael Walker, Matthew P eLife Neuroscience The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package. eLife Sciences Publications, Ltd 2021-10-14 /pmc/articles/PMC8516415/ /pubmed/34648426 http://dx.doi.org/10.7554/eLife.70092 Text en © 2021, Vallat and Walker https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Vallat, Raphael
Walker, Matthew P
An open-source, high-performance tool for automated sleep staging
title An open-source, high-performance tool for automated sleep staging
title_full An open-source, high-performance tool for automated sleep staging
title_fullStr An open-source, high-performance tool for automated sleep staging
title_full_unstemmed An open-source, high-performance tool for automated sleep staging
title_short An open-source, high-performance tool for automated sleep staging
title_sort open-source, high-performance tool for automated sleep staging
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516415/
https://www.ncbi.nlm.nih.gov/pubmed/34648426
http://dx.doi.org/10.7554/eLife.70092
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