Evaluating Injury Severity in Neonatal Encephalopathy Using Automated Quantitative Electroencephalography Analysis: A...

Area of Science:

• Neuroscience
• Biomarkers
• Neonatal care

Background:

• Quantitative electroencephalography (qEEG) shows promise for neonatal encephalopathy (NE) biomarkers.
• Previous qEEG studies in NE lacked generalizability due to manual data processing and individualized feature calculation.

Purpose of the Study:

• To pilot a fully automated qEEG analysis method for neonates with NE undergoing therapeutic hypothermia (TH).
• To investigate associations between automated qEEG features and NE severity, neuroimaging scores, and seizures.

Main Methods:

• Automated calculation of suppression ratio (SR), absolute delta power, and relative delta, theta, alpha, and beta power from EEG data.
• Analysis of qEEG data from 30 neonates with NE receiving 72-hour TH.
• Correlation of qEEG features with modified Sarnat scores and NICHD-NRN neuroimaging scores.

Main Results:

• Higher SR and lower absolute delta power correlated with NE severity (Sarnat score).
• Worse neuroimaging scores (NICHD-NRN) associated with higher SR, lower absolute delta power, and higher relative beta power.
• Automated qEEG features did not significantly correlate with seizure presence post-correction.
• Evidence suggests immature relative frequency composition in neonates with more severe brain injury.

Conclusions:

• Automated qEEG analysis is a generalizable method for NE biomarker discovery and monitoring TH response.
• Automated qEEG may aid in assessing brain maturity in neonates with NE.
• This approach enhances accessibility and consistency in qEEG research for NE.