Deep learning for estimation of functional brain maturation from EEG of premature neonates
Laura P Gschwandtner, M. Hartmann, Lisa Oberdorfer, Franz Fürbass, Katrin Klebermas-Schrehof, Tobias Werther, Nathan J. Stevenson, G. Gritsch, Hannes Perko, Angelika Berger, Tilmann Kluge, Vito Giordano
Abstract
EEG monitoring of early brain function and development in neonatal intensive care units may help to identify infants with high risk of serious neurological impairment and to assess brain maturation for evaluation of neurodevelopmental progress. Automated analysis of EEG data makes continuous evaluation of brain activity fast and accessible. A convolutional neural network (CNN) for regression of EEG maturational age of premature neonates from marginally preprocessed serial EEG recordings is proposed. The CNN was trained and validated using 141 EEG recordings from 43 preterm neonates born below 28 weeks of gestation with normal neurodevelop-mental outcome at 12 months of corrected age. The estimated functional brain maturation between the first and last EEG recording increased in each patient. On average over 96% of repeated measures within an infant had an increasing EEG maturational age according to the post menstrual age at EEG recording time. Our algorithm has potential to be deployed to support neonatologists for accurate estimation of functional brain maturity in premature neonates.