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 frequency bands of 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 language-independent 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.

中文翻译：

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脑电图数据的随机模型参数作为脑疟疾后儿童神经发育的生物标志物。

这项研究的目的是测试脑电图（EEG）记录的统计特征，以预测因脑疟疾引起的昏迷后乌干达儿童的神经发育和认知。脑电时间序列的频带增量被建模为学生过程；对这些Student过程的参数进行了估计，并将其与临床和人口统计学数据一起用于机器学习算法中，以预测脑部疟疾发病6个月后儿童的神经发育和认知得分。这项工作的关键创新在于确定随机脑电图特征，这些特征可以用作与语言无关的标志，以标记脑疟疾对发育中的大脑的影响。