| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-09-11 |
| タイトル |
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タイトル |
Automated Sleep Staging via Parallel Frequency-Cut Attention |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
Sleep |
| キーワード |
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主題Scheme |
Other |
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主題 |
Electroencephalography |
| キーワード |
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主題Scheme |
Other |
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主題 |
Feature extraction |
| キーワード |
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主題Scheme |
Other |
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主題 |
Brain modeling |
| キーワード |
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主題Scheme |
Other |
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主題 |
Time-frequency analysis |
| キーワード |
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主題Scheme |
Other |
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主題 |
Spectrogram |
| キーワード |
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主題Scheme |
Other |
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主題 |
Transformers |
| キーワード |
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主題Scheme |
Other |
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主題 |
Sleep staging |
| キーワード |
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主題Scheme |
Other |
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主題 |
EEG |
| キーワード |
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主題Scheme |
Other |
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主題 |
time-frequency patch |
| キーワード |
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主題Scheme |
Other |
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主題 |
transformer |
| キーワード |
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主題Scheme |
Other |
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主題 |
model interpretability |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Chen, Zheng
Yang, Ziwei
Zhu, Lingwei
Chen, Wei
Tamura, Toshiyo
小野, 直亮
Altaf-Ul-Amin, MD.
金谷, 重彦
Huang, Ming
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Stage-based sleep screening is a widely-used tool in both healthcare and neuroscientific research, as it allows for the accurate assessment of sleep patterns and stages. In this paper, we propose a novel framework that is based on authoritative guidance in sleep medicine and is designed to automatically capture the time-frequency characteristics of sleep electroencephalogram (EEG) signals in order to make staging decisions. Our framework consists of two main phases: a feature extraction process that partitions the input EEG spectrograms into a sequence of time-frequency patches, and a staging phase that searches for correlations between the extracted features and the defining characteristics of sleep stages. To model the staging phase, we utilize a Transformer model with an attention-based module, which allows for the extraction of global contextual relevance among time-frequency patches and the use of this relevance for staging decisions. The proposed method is validated on the large-scale Sleep Heart Health Study dataset and achieves new state-of-the-art results for the wake, N2, and N3 stages, with respective F1 scores of 0.93, 0.88, and 0.87 using only EEG signals. Our method also demonstrates high inter-rater reliability, with a kappa score of 0.80. Moreover, we provide visualizations of the correspondence between sleep staging decisions and features extracted by our method, which enhances the interpretability of the proposal. Overall, our work represents a significant contribution to the field of automated sleep staging and has important implications for both healthcare and neuroscience research. |
| 書誌情報 |
en : IEEE Transactions on Neural Systems and Rehabilitation Engineering
巻 31,
p. 1974-1985,
ページ数 12,
発行日 2023-02-09
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| 出版者 |
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出版者 |
IEEE |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1558-0210 |
| 出版者版DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1109/TNSRE.2023.3243589 |
| 出版者版URI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
URI |
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関連識別子 |
https://ieeexplore.ieee.org/abstract/document/10041186 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| 著者版フラグ |
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出版タイプ |
VoR |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
20K19923 |
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研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20K19923/ |
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研究課題名 |
後天性因子により心臓の健康状態を解釈・推定する基盤技術の研究開発 |
fulltext (18.0 MB)
