Macroperiodic Oscillations Are Associated With Seizures Following Acquired Brain Injury in Young Children

A Comparison of Automatically Extracted Quantitative EEG Features for Seizure Risk Stratification in Neonatal Encephalopathy

PURPOSE

Seizures occur in up to 40% of neonates with neonatal encephalopathy. Earlier identification of seizures leads to more successful seizure treatment, but is often delayed because of limited availability of continuous EEG monitoring. Clinical variables poorly stratify seizure risk, and EEG use to stratify seizure risk has previously been limited by need for manual review and artifact exclusion. The goal of this study is to compare the utility of automatically extracted quantitative EEG (qEEG) features for seizure risk stratification.

METHODS

We conducted a retrospective analysis of neonates with moderate-to-severe neonatal encephalopathy who underwent therapeutic hypothermia at a single center. The first 24 hours of EEG underwent automated artifact removal and qEEG analysis, comparing qEEG features for seizure risk stratification.

RESULTS

The study included 150 neonates and compared the 36 (23%) with seizures with those without. Absolute spectral power best stratified seizure risk with area under the curve ranging from 63% to 71%, followed by range EEG lower and upper margin, median and SD of the range EEG lower margin. No features were significantly more predictive in the hour before seizure onset. Clinical examination was not associated with seizure risk.

CONCLUSIONS

Automatically extracted qEEG features were more predictive than clinical examination in stratifying neonatal seizure risk during therapeutic hypothermia. qEEG represents a potential practical bedside tool to individualize intensity and duration of EEG monitoring and decrease time to seizure recognition. Future work is needed to refine and combine qEEG features to improve risk stratification.

Macroperiodic Oscillations: A Potential Novel Biomarker of Outcome in Neonatal Encephalopathy

PURPOSE

Neonatal encephalopathy (NE) is a common cause of neurodevelopmental morbidity. Tools to accurately predict outcomes after therapeutic hypothermia remain limited. We evaluated a novel EEG biomarker, macroperiodic oscillations (MOs), to predict neurodevelopmental outcomes.

METHODS

We conducted a secondary analysis of a randomized controlled trial of neonates with moderate-to-severe NE who underwent standardized clinical examination, magnetic resonance (MR) scoring, video EEG, and neurodevelopmental assessment with Bayley III evaluation at 18 to 24 months. A non-NE cohort of neonates was also assessed for the presence of MOs. The relationship between clinical examination, MR score, MOs, and neurodevelopmental assessment was analyzed.

RESULTS

The study included 37 neonates with 24 of whom survived and underwent neurodevelopmental assessment (70%). The strength of MOs correlated with severity of clinical encephalopathy. MO strength and spread significantly correlated with Bayley III cognitive percentile ( P = 0.017 and 0.046). MO strength outperformed MR score in predicting a combined adverse outcome of death or disability ( P = 0.019, sensitivity 100%, specificity 77% vs. P = 0.079, sensitivity 100%, specificity 59%).

CONCLUSIONS

MOs are an EEG-derived, quantitative biomarker of neurodevelopmental outcome that outperformed a comprehensive validated MRI injury score and a detailed systematic discharge examination in this small cohort. Future work is needed to validate MOs in a larger cohort and elucidate the underlying pathophysiology of MOs.

Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG

Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.

The Role of Electroencephalography in the Prognostication of Clinical Outcomes in Critically Ill Children: A Review

Electroencephalography (EEG) is a neurologic monitoring modality that allows for the identification of seizures and the understanding of cerebral function. Not only can EEG data provide real-time information about a patient’s clinical status, but providers are increasingly using these results to understand short and long-term prognosis in critical illnesses. Adult studies have explored these associations for many years, and now the focus has turned to applying these concepts to the pediatric literature. The aim of this review is to characterize how EEG can be utilized clinically in pediatric intensive care settings and to highlight the current data available to understand EEG features in association with functional outcomes in children after critical illness. In the evaluation of seizures and seizure burden in children, there is abundant data to suggest that the presence of status epilepticus during illness is associated with poorer outcomes and a higher risk of mortality. There is also emerging evidence indicating that poorly organized EEG backgrounds, lack of normal sleep features and lack of electrographic reactivity to clinical exams portend worse outcomes in this population. Prognostication in pediatric critical illness must be informed by the comprehensive evaluation of a patient’s clinical status but the utilization of EEG may help contribute to this assessment in a meaningful way.

Detecting slow narrowband modulation in EEG signals

BACKGROUND

We observed an unusual modulatory phenomenon in the electroencephalogram (EEG) of pediatric patients with acquired brain injury. The modulation is orders of magnitude slower than the fast EEG background activity, necessitating new analysis procedures to systematically detect and quantify the phenomenon.

NEW METHOD

We propose a method for analyzing spatial and temporal relationships associated with slow, narrowband modulation of EEG. We extract envelope signals from physiological frequency bands of EEG. Then, we construct a sparse representation of the spectral content of the envelope signal across sliding windows. For the latter, we use an augmented LASSO regression to incorporate spatial and temporal filtering into the solution. The method can be applied to windows of variable length, depending on the desired frequency resolution.

RESULTS

The sparse estimates of the envelope power spectra enable the detection of narrowband modulation in the millihertz frequency range. Subsequently, we are able to assess non-stationarity in the frequency and spatial relationships across channels. The method can be paired with unsupervised anomaly detection to identify windows with significant modulation. We validated such findings by applying our method to a control set of EEGs.

COMPARISON WITH EXISTING METHODS

To our knowledge, no methods have been previously proposed to quantify second order modulation at such disparate time-scales.

CONCLUSIONS

We provide a general EEG analysis framework capable of detecting signal content below 0.1Hz, which is especially germane to clinical recordings that may contain multiple hours worth of continuous data.