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We explored the feature extraction techniques for Bayesian assessment of EEG maturity of newborns in the context that the continuity of EEG is the most important feature for assessment of the brain development. The continuity is associated with EEG “stationarity” which we propose to evaluate with adaptive segmentation of EEG into pseudo-stationary intervals. The histograms of these intervals are then...
We use the Bayesian Model Averaging (BMA) over Decision Trees (DTs) for assessing newborn brain maturity from clinical EEG. We found that within this methodology an appreciable part of EEG features is rarely used in the DT models, because these features make weak contribution to the assessment. It was identified that the portion of DT models using weak EEG features is large. The negative impact of...
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