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Parameters of stochastic models for electroencephalogram data as biomarkers for child's neurodevelopment after cerebral malaria
The objective of this study was to test statistical features from the
electroencephalogram (EEG) recordings as predictors of neurodevelopment and
cognition of Ugandan children after coma due to cerebral malaria. The increments of
the EEG time series were modeled as Student processes; the parameters of these
Student processes were estimated and used along with clinical and demographic data
in a machine-learning algorithm for the prediction of children's neurodevelopmental
and cognitive scores 6 months after cerebral malaria illness. The key innovation of this
work is in the identification of stochastic EEG features that can serve as languageindependent
markers of the impact of cerebral malaria on the developing brain. The
results can enhance prognostic determination of which children are in most need of
rehabilitative interventions, which is especially important in resource-constrained
settings such as sub-Saharan Africa.