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Machine Learning for assessment of newborn brain development from sleep EEG signals


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  Dr V Schetinin  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG). Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts’ agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. The project aims to explore new feature extraction techniques for the Bayesian assessment and reliable estimation of predictive distribution in the model’s outcomes. The new models will be verified on a large EEG data set including 1,100 recordings made from newborns. The designed features are expected to be highly correlated with the brain maturation and increase the assessment accuracy.

Research questions: (1) to explore the ability of a designed strategy of Machine Learning to extend the EEG-based brain development assessment (2) to explore ways of designing decision models within the Bayesian framework.

The deadlines are as follows:

For March starters:

International applicants - 30th November 2021

UK nationals - 18th January 2022

For October starters:

International applicants - 30th June 2022

UK nationals - 5th August 2022


[1] Schetinin V, Jakaite L (2017) Extraction of features from sleep EEG for Bayesian assessment of brain development. PLoS ONE 12(3):
[2] Livija Jakaite, Vitaly Schetinin, Carsten Maple, "Bayesian Assessment of Newborn Brain Maturity from Two-Channel Sleep Electroencephalograms", Computational and Mathematical Methods in Medicine, vol. 2012, 7 pages, 2012.
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