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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| Main Authors: | , , |
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| Format: | Book |
| Published: |
IEEE,
2022-01-01T00:00:00Z.
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| Call Number: |
A1234.567 |
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| Copy 1 | Available |