A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram

The classification of sleep stages is an important process. However, this process is time-consuming, subjective, and error-prone. Many automated classification methods use electroencephalogram (EEG) signals for classification. These methods do not classify well enough and perform poorly in the N1 du...

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Detalles Bibliográficos
Autores principales: Li, Chengfan, Qi, Yueyu, Ding, Xuehai, Zhao, Junjuan, Sang, Tian, Lee, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141573/
https://www.ncbi.nlm.nih.gov/pubmed/35627856
http://dx.doi.org/10.3390/ijerph19106322

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141573/
https://www.ncbi.nlm.nih.gov/pubmed/35627856
http://dx.doi.org/10.3390/ijerph19106322

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