Abstract
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.
Highlights
Hypoxic-ischemic encephalopathy (HIE) is one of the most severe birth complications that causes neonatal brain damage
Features with the smallest p-value are shown with boldface, indicating the statistically significant correlation with the cognitive scores. These features were radius calculated from IMF7 (3–4 Hz) and transitivity, global efficiency, and characteristic path length computed from IMF8 (1.5– 3 Hz)
The permutation entropy (PEn) calculated from channel C3 and spectral entropy (SpEn) computed from channels T3 and T5 exhibit significant correlations with the vector of the cognitive scores
Summary
Hypoxic-ischemic encephalopathy (HIE) is one of the most severe birth complications that causes neonatal brain damage. The incidence of HIE is approximately 1–6 per 1000 live births (Byeon et al, 2015). Neurodevelopmental impairment (NDI) is a composite outcome that includes cognitive, behavioral, educational, and motor impairments. Cognitive deficit is considered one of the most expected outcomes associated with NDI, featured by slow information processing speed, deficits in working memory, attention, and executive function (Slaughter et al, 2016). This substantially impacts the affected individual and their families, including education, social participation, employment, and quality of life
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
