5 - Early Quantitative EEG Biomarkers Predict Subclinical Seizures in Neonates with Hypoxic-Ischemic Encephalopathy

Publication Number: 1004.5

Srinivas Kota, University of Texas Southwestern Medical School, Dallas, TX, United States; Marilyn L. Sigman, Parkland Memorial Hospital, North Richland Hills, TX, United States; Agnes J. Todd, Parkland Health & Hospital System, Lancaster, TX, United States; Patti Burchfield, University of Texas Southwestern Medical School, Dallas, TX, United States; Yu-Lun Liu, University of Texas Southwestern Medical School, Dallas, TX, United States; Lina Chalak, Childrens Health Dallas UT Southwestern, DALLAS, TX, United States

Background: Hypoxic-ischemic encephalopathy (HIE) is a leading cause of death and disability in term newborns and accounts for 45-50% of neonatal seizures. A higher seizure burden is associated with adverse neurodevelopmental outcomes. Subclinical seizures are characterized by electrographic epileptiform activity without clinical signs and typically emerge within early hours of life, serving as markers of reperfusion-related brain injury. Despite advances in neonatal neurocritical care, significant knowledge gaps remain in understanding the underlying mechanisms and in predicting seizure risk.

Objective: To assess the predictive value of early qEEG biomarkers for identifying newborns with HIE at risk for seizures during their hospital stay.

Design/Methods: This prospective cohort study enrolled term newborns (≥36 weeks' gestation) with HIE, with severity assessed by modified Sarnat examination. Newborn with moderate or severe HIE received therapeutic hypothermia (TH); those with mild HIE did not. EEG monitoring began within six hours of life. The central-parietal bipolar EEG data (C3-C4, C3-P3, C4-P4, and P3-P4) were considered for calculating power spectral density (PSD) for every 5-minute artifact-free segments. qEEG biomarkers including delta, theta, alpha, beta, and total power were calculated as the area under the PSD curve. Additional biomarkers such as the delta/alpha, the (delta+theta)/alpha, and the spectral edge frequency at 90 percent (SEF 90) were computed. The median qEEG value over the first two-hour recording period was used to predict later seizure risk using receiver operating characteristic (ROC) analysis with the area under the curve (AUC), and logistic regression models reported odds ratios (ORs) with 95% confidence intervals.

Results: A total of 100 term newborns with mild to severe HIE were enrolled (Table 1). TH was administered to half of the cohort (51%), with a significantly higher proportion among newborns with seizures (P < 0.001). Eighteen (12 TH, 4 late TH) newborns experienced seizures. Several early qEEG biomarkers were significantly associated with later seizure risk (Table 2, Figure 1). Lower delta and total power, reduced delta/alpha and (delta + theta)/alpha ratios, and higher SEF90 were predictive of increased seizure risk (P < 0.05).

Conclusion(s): Early qEEG biomarkers can predict seizure risk in newborns with HIE. Identifying high-risk newborns may guide timely therapeutic intervention and improve outcomes. Validation in larger cohorts and bedside integration could enable real-time seizure risk assessment and personalized neuroprotective care.

Table 1 Maternal and Neonatal Demographics
P-values were computed using t-tests or Wilcoxon rank-sum tests for continuous variables, and Fisher's exact test or chi-square tests for categorical variables.
*Indicates statistical significance


Table 2 Univariate ordinal logistic regression models of potential quantitative EEG predictors of subclinical seizures
*Indicates statistical significance

Figure 1
Figure 1. Distribution of quantitative EEG features in newborns without and with seizures (No SZ and SZ groups, respectively). Individual points indicate subject-level values. The corresponding p-values were derived from univariate ordinal logistic regression models assessing the association between each EEG feature and seizure occurrence.