An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation.

Journal: The Lancet. Digital health
Published Date: Dec 1, 2022

Abstract

BACKGROUND: Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors.

Authors

  • Saeed Montazeri Moghadam
    Department of Biomedical Engineering, Amirkabir University of Technology, 424Hafez Ave, Tehran, Iran.
  • Manu Airaksinen
    BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland.
  • Päivi Nevalainen
    BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Viviana Marchi
    Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Fondazione Stella Maris Foundation, Pisa, Italy.
  • Lena Hellström-Westas
    Department of Women's and Children's Health, Uppsala University, Sweden.
  • Nathan J Stevenson
    3 Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
  • Sampsa Vanhatalo
    1 BABA Center, Children's Hospital, HUS Medical Imaging Center, Clinical Neurophysiology, University of Helsinki, Helsinki University Hospital and University of Helsinki, Finland.

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