ArticleJournal of statistical distributions and applications2018
Parameters of stochastic models for electroencephalogram data as biomarkers for child's neurodevelopment after cerebral malaria.
Article in Journal of statistical distributions and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Resting-state functional connectivity does not predict individual differences in the effects of emotion on memory.Scientific reports · 2022Article
- Updates in central nervous system malaria: literature review and considerations.Current opinion in infectious diseases · 2022Review
- Using EEG in Resource-Limited Areas: Comparing Qualitative and Quantitative Interpretation Methods in Cerebral Malaria.Pediatric neurology · 2022Article
- Artificial intelligence and the future of global health.Lancet (London, England) · 2020Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
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