Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning.

Journal: IEEE transactions on bio-medical engineering
Published Date: May 21, 2021

Abstract

BACKGROUND: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging.

Authors

  • Huy Phan
    The Institute for Signal Processing, University of Lübeck, Lübeck, Germany.
  • Oliver Y Chen
  • Philipp Koch
  • Zongqing Lu
  • Ian McLoughlin
    School of Computing, The University of Kent, Medway, Kent, United Kingdom.
  • Alfred Mertins
  • Maarten De Vos
    STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics-Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium. maarten.devos@kuleuven.be.

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