Validation of ‘Somnivore’, a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, Somnivore(TM), for automated wake–sleep stage classification. We...

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Detalles Bibliográficos
Autores principales: Allocca, Giancarlo, Ma, Sherie, Martelli, Davide, Cerri, Matteo, Del Vecchio, Flavia, Bastianini, Stefano, Zoccoli, Giovanna, Amici, Roberto, Morairty, Stephen R., Aulsebrook, Anne E., Blackburn, Shaun, Lesku, John A., Rattenborg, Niels C., Vyssotski, Alexei L., Wams, Emma, Porcheret, Kate, Wulff, Katharina, Foster, Russell, Chan, Julia K. M., Nicholas, Christian L., Freestone, Dean R., Johnston, Leigh A., Gundlach, Andrew L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6431640/
https://www.ncbi.nlm.nih.gov/pubmed/30936820
http://dx.doi.org/10.3389/fnins.2019.00207

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6431640/
https://www.ncbi.nlm.nih.gov/pubmed/30936820
http://dx.doi.org/10.3389/fnins.2019.00207

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