EEG monitoring during anesthesia in children aged 0 to 18 months: amplitude-integrated EEG and age effects

Background

The amplitude-integrated EEG (aEEG) is a widely used monitoring tool in neonatology / pediatric intensive care. It takes into account the amplitudes, but not the frequency composition, of the EEG. Advantages of the aEEG are clear criteria for interpretation and time compression. During the first year of life, the electroencephalogram (EEG) during sedation / anesthesia changes from a low-differentiated to a differentiated EEG; higher-frequency waves develop increasingly. There are few studies on the use of aEEG during pediatric anesthesia. A systematic evaluation of the aEEG in defined EEG stages during anesthesia / sedation is not yet available. Parameters of pediatric EEGs (power, median frequency, spectral edge frequency) recorded during anesthesia and of the corresponding aEEGs (upper and lower value of the aEEG trace) should be examined for age-related changes. Furthermore, it should be examined whether the aEEG can distinguish EEG stages of sedation / anesthesia in differentiated EEGs.

Methods

In a secondary analysis of a prospective observational study EEGs and aEEGs (1-channel recordings, electrode positions on forehead) of 50 children (age: 0–18 months) were evaluated. EEG stages: A (awake), Slow EEG, E2, F0, and F1 in low-differentiated EEGs and A (awake), B0–2, C0–2, D0–2, E0–2, F0–1 in differentiated EEGs.

Results

Median and spectral edge frequency increased significantly with age (p < 0.001 each). In low-differentiated EEGs, the power of the Slow EEG increased significantly with age (p < 0.001). In differentiated EEGs, the power increased significantly with age in each of the EEG stages B1 to E1 (p = 0.04, or less), and the upper and lower values of the aEEG trace increased with age (p < 0.001). A discriminant analysis using the upper and lower values of the aEEG showed that EEG epochs from the stages B1 to E1 were assigned to the original EEG stage in only 19.3% of the cases. When age was added as the third variable, the rate of correct reclassifications was 28.5%.

Conclusions

The aEEG was not suitable for distinguishing EEG stages above the burst suppression range. For this purpose, the frequency composition of the EEG should be taken into account.

Rigor of Neurovascular Coupling (NVC) Assessment in Newborns Using Different Amplitude EEG Algorithms

Birth asphyxia constitutes a major global public health burden for millions of infants with a critical need for real time physiological biomarkers. This proof of concept study targets the translational rigor of such biomarkers and aims to examine whether the variability in the amplitude-integrated EEG (aEEG) outputs impact the determination of neurovascular coupling (NVC) in newborns with encephalopathy. A convenience sample with neonatal asphyxia were monitored for twenty hours in the first day of life with EEG and near infrared spectroscopy (NIRS)-based cerebral tissue oxygen saturation (SctO2). NVC between aEEG and NIRS-SctO2 was assessed using wavelet transform coherence (WTC) analysis, specifically by the wavelet total pixel number of significant coherences within 95% confidence interval. The raw EEG was converted to aEEG using three different methods: Method (M1) derives from the algorithm by Zhang and Ding. Method (M2) uses a Neonatal EEG Analysis Toolbox (WU-NEAT). Method (M3) extracts output directly from a commercial platform with an undisclosed algorithm. Our results demonstrate excellent agreement with Bland Altman comparisons for WTC-based NVC irrespective of the algorithms used, despite significant heterogeneities in the aEEG tracings produced by three algorithms. Our findings confirm the robustness of NVC wavelet analysis in Neonatal Encephalopathy related to HIE.

Features and Prognostic Value of Quantitative Electroencephalogram Changes in Critically Ill and Non-critically Ill Anti-NMDAR Encephalitis Patients: A Pilot Study

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a common cause of encephalitis in intensive care units. Until now, no reliable method has existed for predicting the outcome of anti-NMDAR encephalitis. In this study, we used quantitative electroencephalography (qEEG) to examine the brain function of anti-NMDAR encephalitis patients and assessed its predictive value. Twenty-six patients diagnosed with anti-NMDAR encephalitis were included and grouped according to whether they were treated in intensive care units (14 critically ill vs. 12 non-critically ill). All patients underwent 2-h 10-channel qEEG recordings at the acute stage. Parameters, including amplitude-integrated electroencephalogram (aEEG), spectral edge frequency 95%, total power, power within different frequency bands (δ, θ, α, and β), and percentages of power in specific frequency bands from frontal and parietal areas were calculated with NicoletOne Software and compared between groups. The short-term outcome was death or moderate/severe disability at 3 months after onset, measured with a modified Rankin Scale, and the long-term outcome was death, disability or relapse at 12 months. No differences in qEEG parameters were observed between the critically ill and non-critically ill patients. However, differential anterior-to-posterior alterations in δ and β absolute band power were observed. Logistic regression analysis revealed that a narrower parietal aEEG bandwidth was associated with favorable long-term outcomes (odds ratio, 37.9; P = 0.044), with an optimal cutoff value of 1.7 μV and corresponding sensitivity and specificity of 90.00 and 56.25%, respectively. In a receiver operating characteristic analysis, the area under the curve was 0.7312. In conclusion, the qEEG parameters failed to reflect the clinical severity of anti-NMDAR encephalitis. However, the parietal aEEG bandwidth may separate patients with favorable and poor long-term outcomes in early stages. The underlying mechanisms require further investigation.

Lost in Transition: A Systematic Review of Neonatal Electroencephalography in the Delivery Room-Are We Forgetting an Important Biomarker for Newborn Brain Health?

BACKGROUND

Electroencephalography (EEG) monitoring is routine in neonatal intensive care units (NICUs) for detection of seizures, neurological monitoring of infants following perinatal asphyxia, and increasingly, following preterm delivery. EEG monitoring is not routinely commenced in the delivery room (DR).

OBJECTIVES

To determine the feasibility of recording neonatal EEG in the DR, and to assess its usefulness as a marker of neurological well-being during immediate newborn transition.

METHODS

We performed a systematic stepwise search of PubMed using the following terms: infant, newborns, neonate, DR, afterbirth, transition, and EEG. Only human studies describing EEG monitoring in the first 15 min following delivery were included. Infants of all gestational ages were included.

RESULTS

Two original studies were identified that described EEG monitoring of newborn infants within the DR. Both prospective observational studies used amplitude-integrated EEG (aEEG) monitoring and found it feasible in infants >34 weeks' gestation; however, technical challenges made it difficult to obtain continuous reliable data. Different EEG patterns were identified in uncompromised newborns and those requiring resuscitation.

CONCLUSION

EEG monitoring is possible in the DR and may provide an objective baseline measure of neurological function. Further feasibility studies are required to overcome technical challenges in the DR, but these challenges are not insurmountable with modern technology.