MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks

Abstract Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use. In addition, the performance of such me...

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Autores principales:	Masato Yamabe, Kazumasa Horie, Hiroaki Shiokawa, Hiromasa Funato, Masashi Yanagisawa, Hiroyuki Kitagawa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6e22097d86a6490ba0fd90528be0d541
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spelling	oai:doaj.org-article:6e22097d86a6490ba0fd90528be0d5412021-12-02T15:08:59ZMC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks10.1038/s41598-019-51269-82045-2322https://doaj.org/article/6e22097d86a6490ba0fd90528be0d5412019-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-51269-8https://doaj.org/toc/2045-2322Abstract Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use. In addition, the performance of such methods has generally been evaluated using rather small-scale datasets, and their robustness against individual differences and noise has not been adequately verified. This research proposes a novel automated scoring method named “MC-SleepNet”, which combines two types of deep neural networks. Then, we evaluate its performance using a large-scale dataset that contains 4,200 biological signal records of mice. The experimental results show that MC-SleepNet can automatically score sleep stages with an accuracy of 96.6% and kappa statistic of 0.94. In addition, we confirm that the scoring accuracy does not significantly decrease even if the target biological signals are noisy. These results suggest that MC-SleepNet is very robust against individual differences and noise. To the best of our knowledge, evaluations using such a large-scale dataset (containing 4,200 records) and high scoring accuracy (96.6%) have not been reported in previous related studies.Masato YamabeKazumasa HorieHiroaki ShiokawaHiromasa FunatoMasashi YanagisawaHiroyuki KitagawaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Masato Yamabe Kazumasa Horie Hiroaki Shiokawa Hiromasa Funato Masashi Yanagisawa Hiroyuki Kitagawa MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks
description	Abstract Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use. In addition, the performance of such methods has generally been evaluated using rather small-scale datasets, and their robustness against individual differences and noise has not been adequately verified. This research proposes a novel automated scoring method named “MC-SleepNet”, which combines two types of deep neural networks. Then, we evaluate its performance using a large-scale dataset that contains 4,200 biological signal records of mice. The experimental results show that MC-SleepNet can automatically score sleep stages with an accuracy of 96.6% and kappa statistic of 0.94. In addition, we confirm that the scoring accuracy does not significantly decrease even if the target biological signals are noisy. These results suggest that MC-SleepNet is very robust against individual differences and noise. To the best of our knowledge, evaluations using such a large-scale dataset (containing 4,200 records) and high scoring accuracy (96.6%) have not been reported in previous related studies.
format	article
author	Masato Yamabe Kazumasa Horie Hiroaki Shiokawa Hiromasa Funato Masashi Yanagisawa Hiroyuki Kitagawa
author_facet	Masato Yamabe Kazumasa Horie Hiroaki Shiokawa Hiromasa Funato Masashi Yanagisawa Hiroyuki Kitagawa
author_sort	Masato Yamabe
title	MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks
title_short	MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks
title_full	MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks
title_fullStr	MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks
title_full_unstemmed	MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks
title_sort	mc-sleepnet: large-scale sleep stage scoring in mice by deep neural networks
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/6e22097d86a6490ba0fd90528be0d541
work_keys_str_mv	AT masatoyamabe mcsleepnetlargescalesleepstagescoringinmicebydeepneuralnetworks AT kazumasahorie mcsleepnetlargescalesleepstagescoringinmicebydeepneuralnetworks AT hiroakishiokawa mcsleepnetlargescalesleepstagescoringinmicebydeepneuralnetworks AT hiromasafunato mcsleepnetlargescalesleepstagescoringinmicebydeepneuralnetworks AT masashiyanagisawa mcsleepnetlargescalesleepstagescoringinmicebydeepneuralnetworks AT hiroyukikitagawa mcsleepnetlargescalesleepstagescoringinmicebydeepneuralnetworks
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MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks

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