Neurophysiological aspects of neonatal seizures

EEG in neonatal seizures: where to look and what to see

INTRODUCTION

Neonatal seizures are frequent and carry a detrimental prognostic outlook. Diagnosis is based on EEG confirmation. Classification has recently changed.

AREAS COVERED

We consulted original papers, book chapters, atlases, and reviews to provide a narrative overview on EEG characteristics of neonatal seizures. We searched PubMed, without time restrictions (last visited: 31 May 2022). Additional papers were extracted from the references list of selected papers. We describe the typical neonatal ictal EEG discharges morphology, location, and propagation, together with age-dependent features. Etiology-dependent electroclinical features, when identifiable, are presented for both acute symptomatic neonatal seizures and neonatal-onset epilepsies and developmental/epileptic encephalopathies. The few ictal variables known to predict long-term outcome have been discussed.

EXPERT OPINION

Multimodal neuromonitoring in critically ill newborns, high-density EEG, and functional neuroimaging might increase our insight into the neurophysiological bases of seizures in newborns. Increasing availability of long-term monitoring with conventional video-EEG and automated detection methods will allow clinicians and researchers to gather an ever expanding bulk of clinical and neurophysiological data to enhance accuracy with deep phenotyping. The latest classification proposal represents an input for critically revising our diagnostic abilities with respect to seizure definition, duration, and semiology, possibly further promoting clinical research.

Functional brain maturation and sleep organisation in neonates with congenital heart disease

OBJECTIVE

Neonates with Congenital Heart Disease (CHD) have structural delays in brain development. To evaluate whether functional brain maturation and sleep-wake physiology is also disturbed, the Functional Brain Age (FBA) and sleep organisation on EEG during the neonatal period is investigated.

METHODS

We compared 15 neonates with CHD who underwent multichannel EEG with healthy term newborns of the same postmenstrual age, including subgroup analysis for d-Transposition of the Great Arteries (d-TGA) (n = 8). To estimate FBA, a prediction tool using quantitative EEG features as input, was applied. Second, the EEG was automatically classified into the 4 neonatal sleep stages. Neonates with CHD underwent neurodevelopmental testing using the Bayley Scale of Infant Development-III at 24 months.

RESULTS

Preoperatively, the FBA was delayed in CHD infants and more so in d-TGA infants. The FBA was positively correlated with motor scores. Sleep organisation was significantly altered in neonates with CHD. The duration of the sleep cycle and the proportion of Active Sleep Stage 1 was decreased, again more marked in the d-TGA infants. Neonates with d-TGA spent less time in High Voltage Slow Wave Sleep and more in Tracé Alternant compared to healthy terms. Both FBA and sleep organisation normalised postoperatively. The duration of High Voltage Slow Wave Sleep remained positively correlated with motor scores in d-TGA infants.

INTERPRETATION

Altered early brain function and sleep is present in neonates with CHD. These results are intruiging, as inefficient neonatal sleep has been linked with adverse long-term outcome. Identifying how these rapid alterations in brain function are mitigated through improvements in cerebral oxygenation, surgery, drugs and nutrition may have relevance for clinical practice and outcome.

REM-sleep related hypermotor seizures: Video documentation and ictal source imaging

INTRODUCTION

Rapid eye movement (REM) sleep has an inhibitory effect on epileptiform EEG discharges, and seizures occur extremely rarely in REM sleep.

CASE STUDY

We present the case and video recordings of a 10-year-old boy, with sleep-related hypermotor seizures starting from REM sleep, identified from videoEEG recordings. The semiology comprised intense fear, tachycardia, tachypnea, followed by hypermotor manifestations. Further investigations included brain MRI and source localization of the EEG signals. Multiple antiepileptic drugs were tried, the patient obtaining a good control of the seizures in the last 2.5 years with eslicarbazepine.

DISCUSSION AND CONCLUSION

The ictal EEG source imaging showed seizure onset in the anterior part of the right insula, with propagation to the orbitofrontal area, confirmed by the semiological sequence. Although rare, focal seizures can be triggered by REM sleep and our findings suggest that deficient maturation of brain areas involved in sleep modulation might induce insufficient desynchronization during REM sleep, thus allowing seizure emergence.

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Premature babies are subjected to environmental stresses that can affect brain maturation and cause abnormal neurodevelopmental outcome later in life. Better understanding this link is crucial to developing a clinical tool for early outcome estimation. We defined maturational trajectories between the Electroencephalography (EEG)-derived ‘brain-age’ and postmenstrual age (the age since the last menstrual cycle of the mother) from longitudinal recordings during the baby’s stay in the Neonatal Intensive Care Unit. Data consisted of 224 recordings (65 patients) separated for normal and abnormal outcome at 9–24 months follow-up. Trajectory deviations were compared between outcome groups using the root mean squared error (RMSE) and maximum trajectory deviation (δ^max). 113 features were extracted (per sleep state) to train a data-driven model that estimates brain-age, with the most prominent features identified as potential maturational and outcome-sensitive biomarkers. RMSE and δ^max showed significant differences between outcome groups (cluster-based permutation test, p < 0.05). RMSE had a median (IQR) of 0.75 (0.60–1.35) weeks for normal outcome and 1.35 (1.15–1.55) for abnormal outcome, while δ^max had a median of 0.90 (0.70–1.70) and 1.90 (1.20–2.90) weeks, respectively. Abnormal outcome trajectories were associated with clinically defined dysmature and disorganised EEG patterns, cementing the link between early maturational trajectories and neurodevelopmental outcome.

Amplitude-integrated electroencephalography for neonatal seizure detection. An electrophysiological point of view

Seizures in the newborn are associated with high morbidity and mortality, making their detection and treatment critical. Seizure activity in neonates is often clinically obscured, such that detection of seizures is particularly challenging. Amplitude-integrated EEG is a technique for simplified EEG monitoring that has found an increasing clinical application in neonatal intensive care. Its main value lies in the relative simplicity of interpretation, allowing nonspecialist members of the care team to engage in real-time detection of electrographic seizures. Nevertheless, to avoiding misdiagnosing rhythmic artifacts as seizures, it is necessary to recognize the electrophysiological ictal pattern in the conventional EEG trace available in current devices. The aim of this paper is to discuss the electrophysiological basis of the differentiation of epileptic seizures and extracranial artifacts to avoid misdiagnosis with amplitude-integrated EEG devices.

Predictive factors and prognostic value for status epilepticus in newborns

OBJECTIVES

To evaluate the predictive factors for status epilepticus (SE) in neonates and prognostic factors for patient outcomes in newborns suffering either isolated seizures or SE.

METHODS

A retrospective single-center study from January 2010 to December 2014, included 91 newborns who had neonatal seizures. Among them, 50 newborns experienced SE and 41 newborns presented isolated seizures only. SE was defined as a single seizure lasting more than 15 min or repeated seizures without return to preictal neurological baseline for more than 15 min. Isolated seizures were defined as one single seizure lasting less than 15 min or more seizures with complete recovery of consciousness between seizures. Perinatal and electroclinical data were recorded. Outcomes were evaluated at one year follow up.

RESULTS

In multivariate analysis, the factors identified as being predictive of SE were a severely abnormal initial neurological examination (OR 15.7, 95% CI (3.8-109) p = 0.00075) and hypoglycaemia (OR 6.8, 95% CI (1.5-49.2) p = 0.024), found mostly in newborns with hypoxic-ischemic encephalopathy. When studying our global cohort, SE was found to be a negative prognostic factor for outcome only in univariate analysis. In newborns with isolated seizures only, the postictal clinical examination results were the only independent prognostic factor found, normal results being associated with a more favorable evolution (OR 48.9, 95% CI (7.16-571) p = 0.0003).

CONCLUSION

Two independent risk factors for SE in newborns have been identified: a severely abnormal initial neurological examination and hypoglycaemia. In newborns with isolated seizures, the only positive prognostic factor was found to be a normal postictal clinical examination.

Review of sleep-EEG in preterm and term neonates

Neonatal sleep is a crucial state that involves endogenous driven brain activity, important for neuronal survival and guidance of brain networks. Sequential EEG-sleep analysis in preterm infants provides insights into functional brain integrity and can document deviations of the biologically pre-programmed process of sleep ontogenesis during the neonatal period. Visual assessment of neonatal sleep-EEG, with integration of both cerebral and non-cerebral measures to better define neonatal state, is still considered the gold standard. Electrographic patterns evolve over time and are gradually time locked with behavioural characteristics which allow classification of quiet sleep and active sleep periods during the last 10weeks of gestation. Near term age, the neonate expresses a short ultradian sleep cycle, with two distinct active and quiet sleep, as well as brief periods of transitional or indeterminate sleep. Qualitative assessment of neonatal sleep is however challenged by biological and environmental variables that influence the expression of EEG-sleep patterns and sleep organization. Developing normative EEG-sleep data with the aid of automated analytic methods, can further improve our understanding of extra-uterine brain development and state organization under stressful or pathological conditions. Based on those developmental biomarkers of normal and abnormal brain function, research can be conducted to support and optimise sleep in the NICU, with the ultimate goal to improve therapeutic interventions and neurodevelopmental outcome.

Electrographic Seizures during the Early Postnatal Period in Preterm Infants

OBJECTIVE

To investigate the frequency and characteristics of electrographic seizures in preterm infants in the early postnatal period.

STUDY DESIGN

Infants <32 weeks gestational age (GA) (n = 120) were enrolled for continuous multichannel electroencephalography (EEG) recording initiated as soon as possible after birth and continued for approximately up to 72 hours of age. Electrographic seizures were identified visually, annotated, and analyzed. Quantitative descriptors of the temporal evolution of seizures, including total seizure burden, seizure duration, and maximum seizure burden, were calculated.

RESULTS

Median GA was 28.9 weeks (IQR, 26.6-30.3 weeks) and median birth weight was 1125 g (IQR, 848-1440 g). Six infants (5%; 95% CI, 1.9-10.6%) had electrographic seizures. Median total seizure burden, seizure duration, and maximum seizure burden were 40.3 minutes (IQR, 5.0-117.5 minutes), 49.6 seconds (IQR, 43.4-76.6 seconds), and 10.8 minutes/hour (IQR, 1.6-20.2 minutes/hour), respectively. Seizure burden was highest in 2 infants with significant abnormalities on neuroimaging.

CONCLUSION

Electrographic seizures are infrequent within the first few days of birth in very preterm infants. Seizures in this population are difficult to detect accurately without continuous multichannel EEG monitoring.

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age (PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0. 93), using Sensitivity, Specificity, Detection Factor (DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor (MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1.0, median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

Multiscale Entropy of Electroencephalogram as a Potential Predictor for the Prognosis of Neonatal Seizures

Objective

Increasing animal studies supported the harmful effects of prolonged or frequent neonatal seizures in developing brain, including increased risk of later epilepsy. Various nonlinear analytic measures had been applied to investigate the change of brain complexity with age. This study focuses on clarifying the relationship between later epilepsy and the changes of electroencephalogram (EEG) complexity in neonatal seizures.

Methods

EEG signals from 19 channels of the whole brain from 32 neonates below 2 months old were acquired. The neonates were classified into 3 groups: 9 were normal controls, 9 were neonatal seizures without later epilepsy, and 14 were neonatal seizures with later epilepsy. Sample entropy (SamEn), multiscale entropy (MSE) and complexity index (CI) were analyzed.

Results

Although there was no significant change in SamEn, the CI values showed significantly decreased over Channels C3, C4, and Cz in patients with neonatal seizures and later epilepsy compared with control group. More multifocal epileptiform discharges in EEG, more abnormal neuroimaging findings, and higher incidence of future developmental delay were noted in the group with later epilepsy.

Conclusions

Decreased MSE and CI values in patients with neonatal seizures and later epilepsy may reflect the mixed effects of acute insults, underlying brain immaturity, and prolonged seizures-related injuries. The analysis of MSE and CI can therefore provide a quantifiable and accurate way to decrypt the mystery of neonatal seizures, and could be a promising predictor.