• Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity

      Jakaite, Livija; Schetinin, Vitaly; Schult, Joachim (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)
      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 used as new features for the assessment of EEG maturity. In our experiments, we used Bayesian model averaging over decision trees to differentiate two age groups, each included 110 EEG recordings. The use of the proposed EEG features has shown, on average, a 6% increase in the accuracy of age differentiation.
    • Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation

      Schetinin, Vitaly; Jakaite, Livija; Schult, Joachim (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)
      Clinical experts assess the newborn brain development by analyzing and interpreting maturity-related features in sleep EEGs. Typically, these features widely vary during the sleep hours, and their informativeness can be different in different sleep stages. Normally, the level of muscle and electrode artifacts during the active sleep stage is higher than that during the quiet sleep that could reduce the informative-ness of features extracted from the active stage. In this paper, we use the methodology of Bayesian averaging over Decision Trees (DTs) to assess the newborn brain maturity and explore the informativeness of EEG features extracted from different sleep stages. This methodology has been shown providing the most accurate inference and estimates of uncertainty, while the use of DT models enables to find the EEG features most important for the brain maturity assessment.