A standardised assessment scheme for conventional EEG in preterm infants

Clin Neurophysiol. 2020 Jan;131(1):199-204. doi: 10.1016/j.clinph.2019.09.028. Epub 2019 Nov 9.

Abstract

Objective: To develop a standardised scheme for assessing normal and abnormal electroencephalography (EEG) features of preterm infants. To assess the interobserver agreement of this assessment scheme.

Methods: We created a standardised EEG assessment scheme for 6 different post-menstrual age (PMA) groups using 4 EEG categories. Two experts, not involved in the development of the scheme, evaluated this on 24 infants <32 weeks gestational age (GA) using random 2 hour EEG epochs. Where disagreements were found, the features were checked and modified. Finally, the two experts independently evaluated 2 hour EEG epochs from an additional 12 infants <37 weeks GA. The percentage of agreement was calculated as the ratio of agreements to the sum of agreements plus disagreements.

Results: Good agreement in all patients and EEG feature category was obtained, with a median agreement between 80% and 100% over the 4 EEG assessment categories. No difference was found in agreement rates between the normal and abnormal features (p = 0.959).

Conclusions: We developed a standard EEG assessment scheme for preterm infants that shows good interobserver agreement.

Significance: This will provide information to Neonatal Intensive Care Unit (NICU) staff about brain activity and maturation. We hope this will prove useful for many centres seeking to use neuromonitoring during critical care for preterm infants.

Keywords: Conventional EEG; Multichannel EEG; Neuromonitoring; Preterm EEG; Preterm infants.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Age Factors
  • Electrodes
  • Electroencephalography / methods
  • Electroencephalography / standards*
  • Gestational Age
  • Humans
  • Infant, Newborn
  • Infant, Premature / physiology*
  • Neurophysiological Monitoring / methods
  • Neurophysiological Monitoring / standards*
  • Observer Variation
  • Time Factors