Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification

Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification

A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electroc...

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Bibliographic Details
Main Authors: Xiaopeng Ji, Yan Li, Peng Wen
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Deep learning
graph convolutional networks
sleep stage classification
Online Access:https://ieeexplore.ieee.org/document/9777906/

Internet

https://ieeexplore.ieee.org/document/9777906/

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