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A Few-Shot Learning-Based EEG and Stage Transition Sequence Generator for Improving Sleep Staging Performance
In this study, generative adversarial networks named SleepGAN are proposed to expand the training set for automatic sleep stage classification tasks by generating both electroencephalogram (EEG) epochs and sequence relationships of sleep stages. In order to reach high accuracy, most existing classif...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775526/ https://www.ncbi.nlm.nih.gov/pubmed/36551762 http://dx.doi.org/10.3390/biomedicines10123006 |