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
Published in:	IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 1464 - 1472
Main Authors:	Ji, Xiaopeng, Li, Yan, Wen, Peng
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Aggregates Algorithms Artificial neural networks Classification Classification algorithms Convolution Datasets Deep learning EEG EKG Electrocardiography Electroencephalography Electromyography Feature extraction graph convolutional networks Jumping Manganese Neural networks Sleep sleep stage classification Temporal variations Testing time
ISSN:	1534-4320, 1558-0210, 1558-0210
Online Access:	Get full text
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