Related Experiment Video
Updated: Mar 7, 2026
Continuous Video Electroencephalogram during Hypoxia-Ischemia in Neonatal Mice
Published on: June 11, 2020
Interrater agreement in the interpretation of neonatal electroencephalography in hypoxic-ischemic encephalopathy
Courtney J Wusthoff1, Joseph Sullivan2, Hannah C Glass2
1Divisions of Child Neurology and Neonatal and Developmental Medicine, Stanford University, Palo Alto, California, U.S.A.
Objective:
Research using neonatal electroencephalography (EEG) has been limited by a lack of a standardized classification system and interpretation terminology. In 2013, the American Clinical Neurophysiology Society (ACNS) published a guideline for standardized terminology and categorization in the description of continuous EEG in neonates. We sought to assess interrater agreement for this neonatal EEG categorization system as applied by a group of pediatric neurophysiologists.
Methods:
A total of 60 neonatal EEG studies were collected from three institutions. All EEG segments were from term neonates with hypoxic-ischemic encephalopathy. Three pediatric neurophysiologists independently reviewed each record using the ACNS standardized scoring system. Unweighted kappa values were calculated for interrater agreement of categorical data across multiple observers.
Results:
Interrater agreement was very good for identification of seizures (κ = 0.93, p < 0.001), with perfect agreement in 95% of records (57 of 60). Interrater agreement was moderate for classifying records as normal or having any abnormality (κ = 0.49, p < 0.001), with perfect agreement in 78% of records (47 of 60). Interrater agreement was good in classifying EEG backgrounds on a 5-category scale (normal, excessively discontinuous, burst suppression, status epilepticus, or electrocerebral inactivity) (κ = 0.70, p < 0.001), with perfect agreement in 72% of records (43 of 60). Other specific background features had lower agreement, including voltage (κ = 0.41, p < 0.001), variability (κ = 0.35, p < 0.001), symmetry (κ = 0.18, p = 0.01), presence of abnormal sharp waves (κ < 0.20, p < 0.05), and presence of brief rhythmic discharges (κ < 0.20, p < 0.05).
Significance:
We found good or very good interrater agreement applying the ACNS system for identification of seizures and classification of EEG background. Other specific EEG features showed limited interrater agreement. Of importance to both clinicians and researchers, our findings support using the ACNS system in identifying seizures and classifying backgrounds of neonatal EEG recordings, but also suggest limited reproducibility for certain other EEG features.
