EEG in children, in the laboratory or at the patient's bedside

Interhemispheric coherence of EEG rhythms in children: Maturation and differentiation in corpus callosum dysgenesis

OBJECTIVES

To evaluate the evolution of interhemispheric coherences (ICo) in background and spindle frequency bands during childhood and use it to identify individuals with corpus callosum dysgenesis (CCd).

METHODS

A monocentric cohort of children aged from 0.25 to 15 years old, consisting of 13 children with CCd and 164 without, was analyzed. The ICo of background activity (ICOBckgrdA), sleep spindles (ICOspindles), and their sum (sICO) were calculated. The impact of age, gender, and CC status on the ICo was evaluated, and the sICO was used to discriminate children with or without CCd.

RESULTS

ICOBckgrdA, ICOspindles and sICO increased significantly with age without any effect of gender (p < 10-4), in both groups. The regression equations of the different ICo were stronger, with adjusted R2 values of 0.54, 0.35, and 0.57, respectively. The ICo was lower in children with CCd compared to those without CCd (p < 10-4 for all comparisons). The area under the precision recall curves for predicting CCd using sICO was 0.992 with 98.9 % sensitivity and 87.5 % specificity.

DISCUSSION

ICo of spindles and background activity evolve in parallel to brain maturation and depends on the integrity of the corpus callosum. sICO could be an effective diagnostic biomarker for screening children with interhemispheric dysfunction.

Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment

Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection strategy that performs well in all signals, regardless of the artifact level. Two different methods have been studied: one based on the data distribution and the other based on the energy function, with entropy as its main component. The method based on the data distribution shows poor performance with signals containing high amplitude outliers. On the contrary, the method based on the energy function is more robust to outliers. As it does not depend on the data distribution, it is not affected by artifactual events. A double rejection strategy has been chosen, first on a motion signal (accelerometer or EEG low-pass filtered between 1 and 10 Hz) and then on the EEG signal. The results showed a higher performance when working combining both artifact rejection methods. The energy-based method, to isolate motion artifacts, and the data-distribution-based method, to eliminate the remaining lower amplitude artifacts were used. In conclusion, a new method that proves to be robust for all types of signals is designed.

Technical recommendations and interpretation guidelines for electroencephalography for premature and full-term newborns

Electroencephalography (EEG) of neonatal patients is amongst the most valuable diagnostic and prognostic tool. EEG recordings, acquired at the bedside of infants, evaluate brain function and the maturation of premature and extremely premature infants. Strict conditions of acquisition and interpretation must be respected to guarantee the quality of the EEG and ensure its safety for fragile children. This article provides guidance for EEG acquisition including: (1) the required equipment and devices, (2) the modalities of installation and asepsis precautions, and (3) the digital signal acquisition parameters to use during the recording. The fundamental role of a well-trained technician in supervising the EEG recording is emphasized. In parallel to the acquisition recommendations, we present a guideline for EEG interpretation and reporting. The successive steps of EEG interpretation, from reading the EEG to writing the report, are described. The complexity of the EEG signal in neonates makes artefact detection difficult. Thus, we provide an overview of certain characteristic artefacts and detail the methods for eliminating them.

Child EEG (and maturation)

EEG changes during the perinatal period, infancy, childhood, and adolescence are concomitant with brain growth, myelination, expanding connectivity, and overall maturation, which are particularly fast during the first year of life. EEG aspects of early brain development are accessible in preterm during the third trimester of gestational age, and they evolve to full-term, infancy, and childhood EEG patterns. Each of these age periods shares specific EEG features that reach gross adult outlines in the first year. Interpreting EEG needs therefore a deep knowledge of pathological and normal EEG patterns with their variants belonging to each age range. Recording EEG during these periods also requires adapting the recording techniques to the specific age in order to obtain interpretable records. This chapter describes normal EEG features and variants, characteristic patterns of development, and some patterns that are unusual for age, from the neonatal period to adolescence.

Spikes might precede seizures and predict epilepsy in children with Sturge-Weber syndrome: A pilot study

PURPOSE

Sturge-Weber syndrome (SWS) is a neurocutaneous disorder characterized by a facial port-wine stain, a glaucoma, and a leptomeningeal angioma. Epilepsy occurs in more than 75% of affected children, and seizures occurring in the first year of life are associated with a poor neurological prognosis. The aim of this study was to identify possible predictive markers of epilepsy on electroencephalogram (EEG) performed prior to seizure onset in children with SWS.

METHODS

This study included children with a diagnosis of SWS who had an EEG performed prior to seizure onset. Patients who did not develop epilepsy had a minimum follow-up of 3-years. We compared EEG characteristics of patients who developed epilepsy with patients who did not develop epilepsy by the time of their follow-up.

RESULTS

Eleven children were included in this study. EEG was performed at the median age of 2.1 months (range 1.0-22.1). Six children developed seizures with a time interval between EEG and seizure onset ranging from 2 days to 21 months. EEG background activity was asymmetric in 8 patients, 5 of whom later developed epilepsy. Focal interictal spikes or sharp waves were exclusively recorded in patients who developed later epilepsy (4 out of 6). One of these patients had a supposed false positive EEG as he did not developed epilepsy until 21 months later and one patient had a false negative EEG with seizures occurring 2 days after a normal EEG.

CONCLUSION

Spikes on EEG might be a useful marker to identify patients with SWS at risk of developing epilepsy. Their predictive value should be assessed in larger prospective studies.