Using Bayesian model selection to characterize neonatal EEG recordings

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The brains of premature infants must undergo significant maturation outside of the womb and are thus particularly susceptible to injury. Electroencephalographic (EEG) recordings are an important diagnostic tool in determining if a newborn's brain is functioning normally or if injury has occurred. However, interpreting the recordings is difficult and requires the skills of a trained electroencephelographer. Because these EEG specialists are rare, an automated interpretation of newborn EEG recordings would increase access to an important diagnostic tool for physicians. To automate this procedure, we employ Bayesian probability theory to compute the posterior probability for the EEG features of interest and use the results in a program designed to mimic EEG specialists. Specifically, we will be identifying waveforms of varying frequency and amplitude, as well as periods of flat recordings where brain activity is minimal.

Original languageEnglish
Title of host publicationBayesian Inference and Maximum Entropy Methods in Science and Engineering - 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Pages235-242
Number of pages8
DOIs
StatePublished - Dec 1 2009
Event29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Oxford, MS, United States
Duration: Jul 5 2009Jul 10 2009

Publication series

NameAIP Conference Proceedings
Volume1193
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
CountryUnited States
CityOxford, MS
Period07/5/0907/10/09

Keywords

  • Bayesian probability theory
  • EEG
  • Model selection

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