Quiet sleep detection in preterm infants using deep convolutional neural networks.

Journal: Journal of neural engineering
Published Date: Dec 1, 2018

Abstract

OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation.

Authors

  • Amir Hossein Ansari
    Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium. imec, Leuven, Belgium.
  • Ofelie De Wel
  • Mario Lavanga
  • Alexander Caicedo
    Katholieke Universiteit Leuven.
  • Anneleen Dereymaeker
  • Katrien Jansen
  • Jan Vervisch
  • 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.
  • Gunnar Naulaers
    5 Neonatal Intensive Care Unit, University Hospitals Leuven, Belgium.
  • Sabine Van Huffel
    Katholieke Universiteit Leuven.

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