Early epileptiform EEG activity in infants with tuberous sclerosis complex predicts epilepsy and neurodevelopmental outcomes.

OBJECTIVE: To study the association between timing and characteristics of the first electroencephalography (EEG) with epileptiform discharges (ED-EEG) and epilepsy and neurodevelopment at 24 months in infants with tuberous sclerosis complex (TSC). METHODS: Patients enrolled in the prospective Epileptogenesis in a genetic model of epilepsy - Tuberous sclerosis complex (EPISTOP) trial, had serial EEG monitoring until the age of 24 months. The timing and characteristics of the first ED-EEG were studied in relation to clinical outcome. Epilepsy-related outcomes were analyzed separately in a conventionally followed group (initiation of vigabatrin after seizure onset) and a preventive group (initiation of vigabatrin before seizures, but after appearance of interictal epileptiform discharges [IEDs]). RESULTS: Eighty-three infants with TSC were enrolled at a median age of 28 days (interquartile range [IQR] 14-54). Seventy-nine of 83 patients (95%) developed epileptiform discharges at a median age of 77 days (IQR 23-111). Patients with a pathogenic TSC2 variant were significantly younger (P-value .009) at first ED-EEG and more frequently had multifocal IED (P-value .042) than patients with a pathogenic TSC1 variant. A younger age at first ED-EEG was significantly associated with lower cognitive (P-value .010), language (P-value .001), and motor (P-value .013) developmental quotients at 24 months. In the conventional group, 48 of 60 developed seizures. In this group, the presence of focal slowing on the first ED-EEG was predictive of earlier seizure onset (P-value .030). Earlier recording of epileptiform discharges (P-value .019), especially when multifocal (P-value .026) was associated with higher risk of drug-resistant epilepsy. In the preventive group, timing, distribution of IED, or focal slowing, was not associated with the epilepsy outcomes. However, when multifocal IEDs were present on the first ED-EEG, preventive treatment delayed the onset of seizures significantly (P-value <.001). SIGNIFICANCE: Early EEG findings help to identify TSC infants at risk of severe epilepsy and neurodevelopmental delay and those who may benefit from preventive treatment with vigabatrin.

Prediction of Neurodevelopment in Infants With Tuberous Sclerosis Complex Using Early EEG Characteristics.

Tuberous Sclerosis Complex (TSC) is a multisystem genetic disorder with a high risk of early-onset epilepsy and a high prevalence of neurodevelopmental comorbidities, including intellectual disability and autism spectrum disorder (ASD). Therefore, TSC is an interesting disease model to investigate early biomarkers of neurodevelopmental comorbidities when interventions are favourable. We investigated whether early EEG characteristics can be used to predict neurodevelopment in infants with TSC. The first recorded EEG of 64 infants with TSC, enrolled in the international prospective EPISTOP trial (recorded at a median gestational age 42 4/7 weeks) was first visually assessed. EEG characteristics were correlated with ASD risk based on the ADOS-2 score, and cognitive, language, and motor developmental quotients (Bayley Scales of Infant and Toddler Development III) at the age of 24 months. Quantitative EEG analysis was used to validate the relationship between EEG background abnormalities and ASD risk. An abnormal first EEG (OR = 4.1, p-value = 0.027) and more specifically a dysmature EEG background (OR = 4.6, p-value = 0.017) was associated with a higher probability of ASD traits at the age of 24 months. This association between an early abnormal EEG and ASD risk remained significant in a multivariable model, adjusting for mutation and treatment (adjusted OR = 4.2, p-value = 0.029). A dysmature EEG background was also associated with lower cognitive (p-value = 0.029), language (p-value = 0.001), and motor (p-value = 0.017) developmental quotients at the age of 24 months. Our findings suggest that early EEG characteristics in newborns and infants with TSC can be used to predict neurodevelopmental comorbidities.

Automated EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study.

BACKGROUND: To improve the objective assessment of continuous video-EEG (cEEG) monitoring of neonatal brain function, the aim was to relate automated derived amplitude and duration parameters of the suppressed periods in the EEG background (dynamic Interburst Interval= dIBIs) after neonatal hypoxic-ischemic encephalopathy (HIE) to favourable or adverse neurodevelopmental outcome. METHODS: Nineteen neonates (gestational age 36-41 weeks) with HIE underwent therapeutic hypothermia and had cEEG-monitoring. EEGs were retrospectively analyzed with a previously developed algorithm to detect the dynamic Interburst Intervals. Median duration and amplitude of the dIBIs were calculated at 1 h-intervals. Sensitivity and specificity of automated EEG background grading for favorable and adverse outcomes were assessed at 6 h-intervals. RESULTS: Dynamic IBI values reached the best prognostic value between 18 and 24 h (AUC of 0.93). EEGs with dIBI amplitude ≥15 µV and duration <10 s had a specificity of 100% at 6-12 h for favorable outcome but decreased subsequently to 67% at 25-42 h. Suppressed EEGs with dIBI amplitude <15 µV and duration >10 s were specific for adverse outcome (89-100%) at 18-24 h (n = 10). Extremely low voltage and invariant EEG patterns were indicative of adverse outcome at all time points. CONCLUSIONS: Automated analysis of the suppressed periods in EEG of neonates with HIE undergoing TH provides objective and early prognostic information. This objective tool can be used in a multimodal strategy for outcome assessment. Implementation of this method can facilitate clinical practice, improve risk stratification and aid therapeutic decision-making. A multicenter trial with a quantifiable outcome measure is warranted to confirm the predictive value of this method in a more heterogeneous dataset.

Quiet sleep detection in preterm infants using deep convolutional neural networks.

OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation. APPROACH: In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age. MAIN RESULTS: The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively. SIGNIFICANCE: Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants.

Weighted Performance Metrics for Automatic Neonatal Seizure Detection Using Multiscored EEG Data.

In neonatal intensive care units, there is a need for around the clock monitoring of electroencephalogram (EEG), especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EEG is a challenge, especially when seizure discharges do not meet exact definitions of repetitiveness or evolution in amplitude and frequency. When several readers score seizures independently, disagreement can be high. Commonly used metrics such as good detection rate (GDR) and false alarm rate (FAR) derived from data scored by multiple raters have their limitations. Therefore, new metrics are needed to measure the performance with respect to the different labels. In this paper, instead of defining the labels by consensus or majority voting, popular metrics including GDR, FAR, positive predictive value, sensitivity, specificity, and selectivity are modified such that they can take different scores into account. To this end, 353 hours of EEG data containing seizures from 81 neonates were visually scored by a clinical neurophysiologist, and then processed by an automated seizure detector. The scored seizures were mixed with false detections of an automated seizure detector and were relabeled by three independent EEG readers. Then, all labels were used in the proposed performance metrics and the result was compared with the majority voting technique and showed higher accuracy and robustness for the proposed metrics. Results were confirmed using a bootstrapping test.

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.

Interrater agreement in visual scoring of neonatal seizures based on majority voting on a web-based system: The Neoguard EEG database.

OBJECTIVE: To assess interrater agreement based on majority voting in visual scoring of neonatal seizures. METHODS: An online platform was designed based on a multicentre seizure EEG-database. Consensus decision based on 'majority voting' and interrater agreement was estimated using Fleiss' Kappa. The influences of different factors on agreement were determined. RESULTS: 1919 Events extracted from 280h EEG of 71 neonates were reviewed by 4 raters. Majority voting was applied to assign a seizure/non-seizure classification. 44% of events were classified with high, 36% with moderate, and 20% with poor agreement, resulting in a Kappa value of 0.39. 68% of events were labelled as seizures, and in 46%, all raters were convinced about electrographic seizures. The most common seizure duration was <30s. Raters agreed best for seizures lasting 60-120s. There was a significant difference in electrographic characteristics of seizures versus dubious events, with seizures having longer duration, higher power and amplitude. CONCLUSIONS: There is a wide variability in identifying rhythmic ictal and non-ictal EEG events, and only the most robust ictal patterns are consistently agreed upon. Database composition and electrographic characteristics are important factors that influence interrater agreement. SIGNIFICANCE: The use of well-described databases and input of different experts will improve neonatal EEG interpretation and help to develop uniform seizure definitions, useful for evidence-based studies of seizure recognition and management.

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 ([Formula: see text] 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 [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median [Formula: see text], 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 [Formula: see text]). 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.

Data-driven metric representing the maturation of preterm EEG.

Essential information about early brain maturation can be retrieved from the preterm human electroencephalogram (EEG). This study proposes a new set of quantitative features that correlate with early maturation. We exploit the known early trend in EEG content from intermittent to continuous activity, which changes the line length content of the EEG. The developmental shift can be captured in the line length histogram, which we use to obtain 28 features; 20 histogram bins and 8 other statistical measurements. Using the mutual information, we select 6 features with high correlation to the infant's age. This subset appears promising to detect deviances from normal brain maturation. The presented data-driven index holds promise for developing into a computational EEG index of maturation that is highly needed for overall assessment in the Neonatal Intensive Care Units.

Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings.

OBJECTIVE: EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection. METHODS: Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1-34.4)weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods. RESULTS: The line length-based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00-7.11)s, maximum IBI duration 14.02 (8.73-18.80)s and burst percentage 48.89 (35.45-60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%. CONCLUSION: Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU. SIGNIFICANCE: This study takes a first step towards fully automatic analysis of the preterm brain.

Interhemispheric synchrony in the neonatal EEG revisited: activation synchrony index as a promising classifier.

A key feature of normal neonatal EEG at term age is interhemispheric synchrony (IHS), which refers to the temporal co-incidence of bursting across hemispheres during trace alternant EEG activity. The assessment of IHS in both clinical and scientific work relies on visual, qualitative EEG assessment without clearly quantifiable definitions. A quantitative measure, activation synchrony index (ASI), was recently shown to perform well as compared to visual assessments. The present study was set out to test whether IHS is stable enough for clinical use, and whether it could be an objective feature of EEG normality. We analyzed 31 neonatal EEG recordings that had been clinically classified as normal (n = 14) or abnormal (n = 17) using holistic, conventional visual criteria including amplitude, focal differences, qualitative synchrony, and focal abnormalities. We selected 20-min epochs of discontinuous background pattern. ASI values were computed separately for different channel pair combinations and window lengths to define them for the optimal ASI intraindividual stability. Finally, ROC curves were computed to find trade-offs related to compromised data lengths, a common challenge in neonatal EEG studies. Using the average of four consecutive 2.5-min epochs in the centro-occipital bipolar derivations gave ASI estimates that very accurately distinguished babies clinically classified as normal vs. abnormal. It was even possible to draw a cut-off limit (ASI~3.6) which correctly classified the EEGs in 97% of all cases. Finally, we showed that compromising the length of EEG segments from 20 to 5 min leads to increased variability in ASI-based classification. Our findings support the prior literature that IHS is an important feature of normal neonatal brain function. We show that ASI may provide diagnostic value even at individual level, which strongly supports its use in prospective clinical studies on neonatal EEG as well as in the feature set of upcoming EEG classifiers.

Bathing epilepsy: a video case of an autonomic seizure.

A six-year-old Moroccan boy experienced nausea, paleness and oral automatisms after almost every shower. A clinical diagnosis of bathing epilepsy was assumed. A video-EEG recording was taken during and after a shower and confirmed ictal high voltage repetitive slow waves over the left temporal lobe. Bathing epilepsy or water immersion epilepsy is a rare form of reflex epilepsy often presenting with autonomic seizures. The onset is usually in the first year of life and the evolution is benign. [Published with video sequences].