Cargando…

GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requ...

Descripción completa

Detalles Bibliográficos
Autores principales: Gao, Tianxiang, Li, Jiayi, Watanabe, Yuji, Hung, Chijung, Yamanaka, Akihiro, Horie, Kazumasa, Yanagisawa, Masashi, Ohsawa, Masahiro, Kume, Kazuhiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628800/
https://www.ncbi.nlm.nih.gov/pubmed/34842647
http://dx.doi.org/10.3390/clockssleep3040041
_version_ 1784607074181709824
author Gao, Tianxiang
Li, Jiayi
Watanabe, Yuji
Hung, Chijung
Yamanaka, Akihiro
Horie, Kazumasa
Yanagisawa, Masashi
Ohsawa, Masahiro
Kume, Kazuhiko
author_facet Gao, Tianxiang
Li, Jiayi
Watanabe, Yuji
Hung, Chijung
Yamanaka, Akihiro
Horie, Kazumasa
Yanagisawa, Masashi
Ohsawa, Masahiro
Kume, Kazuhiko
author_sort Gao, Tianxiang
collection PubMed
description Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse’s data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.
format Online
Article
Text
id pubmed-8628800
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-86288002021-11-30 GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm Gao, Tianxiang Li, Jiayi Watanabe, Yuji Hung, Chijung Yamanaka, Akihiro Horie, Kazumasa Yanagisawa, Masashi Ohsawa, Masahiro Kume, Kazuhiko Clocks Sleep Article Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse’s data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available. MDPI 2021-11-01 /pmc/articles/PMC8628800/ /pubmed/34842647 http://dx.doi.org/10.3390/clockssleep3040041 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gao, Tianxiang
Li, Jiayi
Watanabe, Yuji
Hung, Chijung
Yamanaka, Akihiro
Horie, Kazumasa
Yanagisawa, Masashi
Ohsawa, Masahiro
Kume, Kazuhiko
GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm
title GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm
title_full GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm
title_fullStr GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm
title_full_unstemmed GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm
title_short GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm
title_sort gi-sleepnet: a highly versatile image-based sleep classification using a deep learning algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628800/
https://www.ncbi.nlm.nih.gov/pubmed/34842647
http://dx.doi.org/10.3390/clockssleep3040041
work_keys_str_mv AT gaotianxiang gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm
AT lijiayi gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm
AT watanabeyuji gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm
AT hungchijung gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm
AT yamanakaakihiro gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm
AT horiekazumasa gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm
AT yanagisawamasashi gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm
AT ohsawamasahiro gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm
AT kumekazuhiko gisleepnetahighlyversatileimagebasedsleepclassificationusingadeeplearningalgorithm