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R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset.
http://hdl.handle.net/10422/00013305
http://hdl.handle.net/10422/0001330557a14ff2-08fd-4a19-96df-f0e14ded32f0
名前 / ファイル | ライセンス | アクション |
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j.clinph.2022.04.012 (1.6 MB)
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This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Item type | 学術雑誌論文 / Journal Article(1) | |||||||||||
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公開日 | 2022-05-30 | |||||||||||
タイトル | ||||||||||||
タイトル | R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset. | |||||||||||
言語 | ||||||||||||
言語 | eng | |||||||||||
キーワード | ||||||||||||
言語 | en | |||||||||||
主題Scheme | Other | |||||||||||
主題 | Sleep apnea syndrome | |||||||||||
キーワード | ||||||||||||
言語 | en | |||||||||||
主題Scheme | Other | |||||||||||
主題 | Wearable sensor | |||||||||||
キーワード | ||||||||||||
言語 | en | |||||||||||
主題Scheme | Other | |||||||||||
主題 | Machine learning | |||||||||||
キーワード | ||||||||||||
言語 | en | |||||||||||
主題Scheme | Other | |||||||||||
主題 | Long short-term memory | |||||||||||
キーワード | ||||||||||||
言語 | en | |||||||||||
主題Scheme | Other | |||||||||||
主題 | Telemedicine | |||||||||||
資源タイプ | ||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
資源タイプ | journal article | |||||||||||
著者 |
IWASAKI, Ayako
× IWASAKI, Ayako× FUJIWARA, Koichi× NAKAYAMA, Chikao× SUMI, Yukiyoshi× KANO, Manabu× NAGAMOTO, Tetsuharu× 角谷, 寛
WEKO
6474
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著者別名 |
角, 幸頼
× 角, 幸頼× 角谷, 寛
WEKO
6474
|
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | Objective: Easily detecting patients with undiagnosed sleep apnea syndrome (SAS) requires a home-use SAS screening system. In this study, we validate a previously developed SAS screening methodology using a large clinical polysomnography (PSG) dataset (N = 938). |
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | Methods: We combined R-R interval (RRI) and long short-term memory (LSTM), a type of recurrent neural networks, and created a model to discriminate respiratory conditions using the training dataset (N = 468). Its performance was validated using the validation dataset (N = 470). |
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | Results: Our method screened patients with severe SAS (apnea hypopnea index; AHI ≥ 30) with an area under the curve (AUC) of 0.92, a sensitivity of 0.80, and a specificity of 0.84. In addition, the model screened patients with moderate/severe SAS (AHI ≥ 15) with an AUC of 0.89, a sensitivity of 0.75, and a specificity of 0.87. |
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | Conclusions: Our method achieved high screening performance when applied to a large clinical dataset. |
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | Significance: Our method can help realize an easy-to-use SAS screening system because RRI data can be easily measured with a wearable heart rate sensor. It has been validated on a large dataset including subjects with various backgrounds and is expected to perform well in real-world clinical practice. |
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書誌情報 |
en : Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 巻 139, p. 80-89, 発行日 2022-04-30 |
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出版者 | ||||||||||||
出版者 | Elsevier B.V. | |||||||||||
ISSN | ||||||||||||
収録物識別子タイプ | ISSN | |||||||||||
収録物識別子 | 1872-8952 | |||||||||||
PMID | ||||||||||||
関連タイプ | isIdenticalTo | |||||||||||
識別子タイプ | PMID | |||||||||||
関連識別子 | 35569296 | |||||||||||
DOI | ||||||||||||
関連タイプ | isIdenticalTo | |||||||||||
識別子タイプ | DOI | |||||||||||
関連識別子 | https://doi.org/10.1016/j.clinph.2022.04.012 | |||||||||||
関連名称 | 10.1016/j.clinph.2022.04.012 | |||||||||||
権利 | ||||||||||||
権利情報 | © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved. | |||||||||||
フォーマット | ||||||||||||
内容記述タイプ | Other | |||||||||||
内容記述 | ||||||||||||
著者版フラグ | ||||||||||||
出版タイプ | VoR | |||||||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
資源タイプ | ||||||||||||
内容記述タイプ | Other | |||||||||||
内容記述 | Journal Article |