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Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity

  • University of Hamburg
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Open access

Abstract

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 25/08/2011

Publication status

Published - 25/08/2011

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781457711893

ISBN (Electronic)

9781457711893

External Publication IDs

  • handle.net: 10547/279174
  • Scopus: 80053018638

Host publication title

nan

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