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

• Published in: Frontiers in neuroscience Vol. 13; p. 207
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 18.03.2019; Frontiers Media S.A
• Subjects: Agreements; Algorithms; Animal cognition; Artificial intelligence; Automation; Classification; Cognitive ability; Data collection; Engineers; Laboratories; Learning algorithms; Machine learning; machine learning algorithms; Medical research; Mental health; Narcolepsy; Neuroscience; Neurosciences; Physiology; polysomnography; Reclassification; signal processing algorithms; Sleep; Sleep and wakefulness; sleep stage classification; Software; Statistical analysis; wake-sleep stage scoring; Australia; Italy; signal processing algorithms; polysomnography; sleep stage classification; wake–sleep stage scoring; machine learning algorithms

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 , for automated wake-sleep stage classification. We de...