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This paper analyzes the Machine Learning approach to classifying sleep electroencephalograms recorded from newborns at risk of brain pathologies. The newborns were in different age groups counted in weeks of post-conceptional age. We consider solutions of the EEG task as a multiclass problem which can be resolved with Decision Tree models, which efficiently predict the weeks of PCA in terms of accuracy. The efficient solution to the multiclass tasks is difficult to find as decision models have to be explored in an ample model parameter space. Moreover, the sleep EEGs have significant overlap between ages because of variations in the newborn maturation patterns and expert evaluations. The experimental results have demonstrated the ability of ML technologies to provide classification accuracy comparable with the expert's opinions.

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