Parameters of stochastic models for electroencephalogram data as biomarkers for child's neurodevelopment after cerebral malaria

Journal of Statistical Distributions and Applications. 2018. Vol. 5:8. P. 1–12.

Veretennikova M., Sikorskii A., Boivin M.

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.

Keywords: EEG, wavelets, stochastic, regularization, stochastic modelling