Ssleepnet: a structured sleep network for sleep staging based on sleep apnea severity

Ssleepnet: a structured sleep network for sleep staging based on sleep apnea severity

Abstract Sleep stage classification is essential in evaluating sleep quality. Sleep disorders disrupt the periodicity of sleep stages, especially the common obstructive sleep apnea (OSA). Many methods only consider how to effectively extract features from physiological signals to classify sleep stag...

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Bibliographic Details
Main Authors: Xingfeng Lv, Jun Ma, Jinbao Li, Qianqian Ren
Format: Article
Language:English
Published: Springer 2023-12-01
Series:Complex & Intelligent Systems
Subjects:
Sleep stage
Obstructive sleep apnea
Deep learning
Structured learning
Online Access:https://doi.org/10.1007/s40747-023-01290-2

Internet

https://doi.org/10.1007/s40747-023-01290-2

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