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

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

Abstract Background Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-related disorders. To alleviate the time con...

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Bibliographic Details
Main Authors: Ye Yuan, Kebin Jia, Fenglong Ma, Guangxu Xun, Yaqing Wang, Lu Su, Aidong Zhang
Format: Article
Language:English
Published: BMC 2019-12-01
Series:BMC Bioinformatics
Subjects:
Attention mechanism
Deep learning
Sleep stage classification
Polysomnography
Multivariate time series
Online Access:https://doi.org/10.1186/s12859-019-3075-z

Internet

https://doi.org/10.1186/s12859-019-3075-z

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