A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants.

Journal: Journal of neural engineering
Published Date: Jan 14, 2020

Abstract

OBJECTIVE: To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age.

Authors

  • Amir H Ansari
    1 Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium.
  • Ofelie De Wel
  • Kirubin Pillay
  • Anneleen Dereymaeker
  • Katrien Jansen
  • Sabine Van Huffel
    Katholieke Universiteit Leuven.
  • Gunnar Naulaers
    5 Neonatal Intensive Care Unit, University Hospitals Leuven, Belgium.
  • 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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