A Bayesian Approach to the Characterization of Electroencephalographic Recordings in Premature Infants

Preterm infants are particularly susceptible to cerebral injury, and electroencephalographic: EEG) recordings provide an important diagnostic tool for determining cerebral health. However, interpreting these EEG recordings is challenging and requires the skills of a trained electroencephalographer. 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 a novel Bayesian approach to compute the probability of EEG features: waveforms) including suppression, delta brushes, and delta waves. The power of this approach lies not only in its ability to closely mimic the techniques used by EEG specialists, but also its ability to be generalized to identify other waveforms that may be of interest for future work. The results of these calculations are used in a program designed to output simple statistics related to the presence or absence of such features. Direct comparison of the software with expert human readers has indicated satisfactory performance, and the algorithm has shown promise in its ability to distinguish between infants with normal neurodevelopmental outcome and those with poor neurodevelopmental outcome.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Year dc:date.available

2011

Author dc:creator

• Mitchell, Timothy

Contributors dc:contributor

• Jeffrey Neil

• Physics
• Bayesian
• EEG
• Premature Infants

Language dc:language

English (en)

DOI

https://doi.org/10.7936/K7ZW1J1B

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:etd-1247