Automated EEG analysis to quantify brain function in preterm and term neonates

This PhD project aims to define specific EEG maturational features in premature infants and to develop an objective scoring system for predicting neurodevelopmental outcome at 2 years of corrected age. Differentiation with transient EEG changes willl give insight in causal factors and timing of brain injury in premature en term infants, which allows to improve neuroprotective measures. Development and implementation of algorithms may contribute to reliable interpretation by non- EEG experts and will enable the implementation of multichannel EEG as a standard investigation in neonatal intensive care units. This clinical study will focus on the quantification, interpretation and classification of (ab)normal maturational EEG features in premature infants. The first part of this study is aimed at the automation of EEG analysis. For automatic quantification and algorithm development of clinically relevant patterns in the background EEG of premature babies, we will collaborate with engineers of KUL ESAT-SISTA. The second part is aimed at the identification of quantitative measures which are sensitive and specific for predicting neurodevelopmental outcome; therefore we will analyze EEG data of both prerterm and term infants. We will do an assessment of brain function by EEG in neonates who experience acute interference with cerebral integrity (peripartal asphyxia, seizures, flow metabolism coupling).  On the other hand, we will measure brain maturation in premature infants by consecutive measurements.