Extracting continuous sleep depth from EEG data without machine learning

The human sleep-cycle has been divided into discrete sleep stages that can be recognized in electroencephalographic (EEG) and other bio-signals by trained specialists or machine learning systems. It is however unclear whether these human-defined stages can be re-discovered with unsupervised methods...

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Detalles Bibliográficos
Autores principales: Metzner, Claus, Schilling, Achim, Traxdorf, Maximilian, Schulze, Holger, Tziridis, Konstantin, Krauss, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238579/
https://www.ncbi.nlm.nih.gov/pubmed/37275555
http://dx.doi.org/10.1016/j.nbscr.2023.100097

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238579/
https://www.ncbi.nlm.nih.gov/pubmed/37275555
http://dx.doi.org/10.1016/j.nbscr.2023.100097

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