AUTHOR=Alotaibi Noura , Bakheet Dalal , Konn Daniel , Vollmer Brigitte , Maharatna Koushik 

TITLE=Cognitive Outcome Prediction in Infants With Neonatal Hypoxic-Ischemic Encephalopathy Based on Functional Connectivity and Complexity of the Electroencephalography Signal

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2021.795006

DOI=10.3389/fnhum.2021.795006

ISSN=1662-5161

ABSTRACT=A high risk of neurodevelopmental outcomes is a major concern for parents, clinicians, and society since it has serious health and socio-economic consequences. This study aims to investigate the potential benefits of using the advanced quantitative electroencephalography analysis (qEEG) for the early prediction of later cognitive outcomes emerging at two years of age. An experimental EEG data were recorded within the first week after birth from a cohort of twenty infants born with hypoxic-ischemic encephalopathy (HIE). A proposed regression framework was based on two different sets of features, namely graph-theoretical features derived from the weighted phase-lag index (WPLI) and entropies metrics represented by sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn). Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain. Correlation analysis showed a significant association in the delta band between proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen, SpEn) and the cognitive profile. These features were used to train and test the tree ensemble (boosted and bagged) regression models. The highest prediction performance was reached to 14.27 root mean square error score (RMSE), 12.07 mean absolute error (MAE) and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results have demonstrated that the proposed qEEG features could show the state of brain development at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate providing the targeted intervention.