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...

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Detalles Bibliográficos
Autores principales: You, Yuyang, Guo, Xiaoyu, Zhong, Xuyang, Yang, Zhihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775526/
https://www.ncbi.nlm.nih.gov/pubmed/36551762
http://dx.doi.org/10.3390/biomedicines10123006

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