A hybrid self-attention deep learning framework for multivariate sleep stage classification.

Yuan, Ye; Jia, Kebin; Ma, Fenglong; Xun, Guangxu; Wang, Yaqing; Su, Lu; Zhang, Aidong

Yuan, Ye; Jia, Kebin; Ma, Fenglong; Xun, Guangxu; Wang, Yaqing; Su, Lu; Zhang, Aidong.

Afiliação

Yuan Y; College of Information and Communication Engineering, Beijing University of Technology, Beijing, China.
Jia K; Beijing Laboratory of Advanced Information Networks, Beijing, China.
Ma F; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China.
Xun G; College of Information and Communication Engineering, Beijing University of Technology, Beijing, China. kebinj@bjut.edu.cn.
Wang Y; Beijing Laboratory of Advanced Information Networks, Beijing, China. kebinj@bjut.edu.cn.
Su L; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China. kebinj@bjut.edu.cn.
Zhang A; Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA.

BMC Bioinformatics ; 20(Suppl 16): 586, 2019 Dec 02.

Article em En | MEDLINE | ID: mdl-31787093

Assuntos

Algoritmos; Aprendizado Profundo; Fases do Sono/fisiologia; Bases de Dados como Assunto; Eletroencefalografia/métodos; Humanos; Análise Multivariada; Polissonografia; Curva ROC

Palavras-chave

Attention mechanism; Deep learning; Multivariate time series; Polysomnography; Sleep stage classification

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fases do Sono / Algoritmos / Aprendizado Profundo Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China

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