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

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, SomnivoreTM, for automated wake–sleep stage classification. We d...

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Bibliographic Details
Main Authors: Giancarlo Allocca, Sherie Ma, Davide Martelli, Matteo Cerri, Flavia Del Vecchio, Stefano Bastianini, Giovanna Zoccoli, Roberto Amici, Stephen R. Morairty, Anne E. Aulsebrook, Shaun Blackburn, John A. Lesku, Niels C. Rattenborg, Alexei L. Vyssotski, Emma Wams, Kate Porcheret, Katharina Wulff, Russell Foster, Julia K. M. Chan, Christian L. Nicholas, Dean R. Freestone, Leigh A. Johnston, Andrew L. Gundlach
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-03-01
Series:Frontiers in Neuroscience
Subjects:
machine learning algorithms
polysomnography
signal processing algorithms
sleep stage classification
wake–sleep stage scoring
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2019.00207/full

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