Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis (qEEG) for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal 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 the proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen and SpEn) from the neonatal EEG data and the cognitive development at age two years. 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 (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 demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention.

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This study was carried out to investigate whether the quantitative analysis of electroencephalogram (EEG) signals of infants with hypoxic-ischemic encephalopathy (HIE) can be used for early prediction of cerebral palsy (CP). We computed sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn) measures to reflect the signal's complexity and the graph-theoretic parameters derived from weighted phase-lag index (WPLI) to measure functional brain connectivity. Both feature sets were calculated in the noise-assisted multivariate empirical mode decomposition (N-A MEMD) domain to characterize the tempo-spectral integration of information and thus provide novel insight into the physiological mechanisms of the brain. Statistical analysis results showed a general deficit in the CP signals at the alpha-band component characterized by a decrease in the complexity measures and an increase in the graph-theoretic parameters specified by the diameter feature. The proposed set of features have also been evaluated using the random under-sampling boosting (RUSBoost) classifier, which was trained and tested on the feature vectors of a cohort of 26 infants -6 who developed CP by the age of 24 months and 20 with normal neuromotor outcome. A good performance of 84.6% classification accuracy (ACC), 83% sensitivity (SNS), 85% specificity (SPC) and 0.87 area under curve (AUC) was obtained using the entropy features extracted from the alpha-band component. A close result of 80.8% ACC, 67% SNS, 85% SPC and 0.79 AUC was also achieved using the diameter feature calculated from the same frequency range. Therefore, it was concluded that the obtained brain functions' characteristics successfully discriminate between the two groups of infants. These characteristics could be considered potential biomarkers of cerebral cellular damage and, therefore, could be employed in practical clinical applications for early CP prediction.INDEX TERMS Brain connectivity, cerebral palsy (CP), electroencephalogram (EEG), empirical mode decomposition (EMD), hypoxic-ischemic encephalopathy (HIE), graph theory, noise-assisted multivariate empirical mode decomposition (N-A MEMD), weighted phase-lag index (WPLI).

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