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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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 |
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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 |
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