Neuromonitoring in Neonatal-Onset Epileptic Encephalopathies

Regina Trollmann1

  • 1Department of Pediatrics and Pediatric Neurology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Frontiers in Neurology
|February 19, 2021
PubMed

Insights

Neonatal neuromonitoring using electroencephalography (EEG) and amplitude-integrated EEG (aEEG) is crucial for identifying seizures in high-risk infants. While valuable, EEG/aEEG has limitations and requires long-term studies for better classification of neonatal epileptic encephalopathies.

Area of Science:

  • Neonatal neurology
  • Clinical neurophysiology
  • Pediatric epilepsy

Background:

  • Neonatal-onset epileptic encephalopathies (EE) have diverse causes and poor neurodevelopmental outcomes.
  • Early neuromonitoring is vital for at-risk neonates.
  • Electroencephalography (EEG) and amplitude-integrated EEG (aEEG) are key monitoring tools.

Purpose of the Study:

  • To highlight the importance of EEG/aEEG in monitoring neonatal epileptic encephalopathies.
  • To discuss the role of EEG/aEEG in hypoxic-ischemic encephalopathy (HIE) management.
  • To emphasize the need for further research in classifying EE subtypes.

Main Methods:

  • Utilizing EEG and aEEG for bedside monitoring of neonates.
  • Analyzing EEG/aEEG data in neonates with HIE undergoing therapeutic hypothermia.
  • Correlating EEG/aEEG findings with clinical outcomes and neuroimaging (cMRI).

Main Results:

  • EEG/aEEG effectively identifies electrographic seizures and abnormal background activity.
  • Burst suppression pattern in HIE neonates under hypothermia correlates with good outcomes in ~40% of cases.
  • Prognostic specificity of EEG/aEEG is lower than cMRI; prolonged monitoring is recommended post-hypothermia.

Conclusions:

  • EEG/aEEG is an essential adjunctive tool for monitoring neonatal epileptic encephalopathies and HIE.
  • While specific genetic variants may show EEG patterns, general classification requires further long-term studies.
  • Continued research is needed to define and classify electro-clinical patterns for improved diagnosis and management.

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