Robust, automated sleep scoring by a compact neural network with distributional shift correction


Autoři: Zeke Barger aff001;  Charles G. Frye aff001;  Danqian Liu aff003;  Yang Dan aff001;  Kristofer E. Bouchard aff001
Působiště autorů: Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America aff001;  Redwood Center for Theoretical Neuroscience, University of California, Berkeley, California, United States of America aff002;  Department of Molecular and Cellular Biology, Howard Hughes Medical Institute, University of California, Berkeley, California, United States of America aff003;  Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America aff004
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224642

Souhrn

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

Klíčová slova:

Algorithms – Electroencephalography – Electromyography – Machine learning – Machine learning algorithms – Neural networks – Preprocessing – Sleep


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Článek vyšel v časopise

PLOS One


2019 Číslo 12