A practical, simple, and useful method of categorizing interictal EEG features in children

Pediatric epilepsy syndromes with associated developmental impairment

In 2022, the International League Against Epilepsy revised their classification of epilepsy syndromes for clinicians to better understand the relationships between different epilepsy syndromes, their underlying causes, and their associated developmental and behavioral features. This review highlights portions of the current classification with an emphasis on epilepsy syndromes that readily present with developmental challenges and provides a unique framework, based on electroencephalography, to easily identify and understand these syndromes. Included in this review are a helpful categorization scheme with visual aid, descriptions of updated epilepsy syndromes, figures of relevant identifiers of syndrome and information regarding future directions toward treatment and research. Covered syndromes include developmental and epileptic encephalopathy, Dravet syndrome, Rasmussen syndrome, and infantile epileptic spasm syndrome, among others. WHAT THIS PAPER ADDS: The revised epilepsy syndrome classification by the International League Against Epilepsy aims to improve the outcomes for children with epilepsy. The electroencephalography features of epilepsy syndromes are grouped based on a categorization model. This model allows clinicians to understand overlapping phenotypes and aid with both identification and diagnosis.

The Clinical Characteristics and Outcomes of Infantile Seizures in the First Year of Life: A Single-Center Study

BACKGROUND

Epilepsy has a high incidence among infants during their first year of life, yet the prognosis can vary significantly. Although considerable research has been conducted on infantile spasms, studies examining infantile-onset epilepsy, excluding infantile spasms, remain limited, particularly concerning the factors influencing outcomes. Therefore, our study aims to elucidate seizure control, developmental outcomes, and prognostic factors in infants with epilepsy during their first year of life, within a single-center study in Malaysia.

METHODS

We retrieved data from patients who experienced seizures before age 12 months and were followed for over two years, using electronic patient records at Hospital Raja Perempuan Zainab II in Kelantan, a state in Malaysia's east coast. We retrospectively reviewed these records and assessed clinical outcomes based on the last follow-up.

RESULTS

Of 75 patients, 61 (81.3%) achieved good seizure control or remission. At the last follow-up, 24 (32%) exhibited developmental delay, whereas 19 (25.3%) displayed abnormal neuroimaging. Patients with abnormal background electroencephalographic (EEG) activity, as well as abnormal radiological findings, were more likely to experience poor seizure control and unfavorable developmental outcomes (P < 0.05).

CONCLUSIONS

Our study underscores that most infants with epilepsy can achieve seizure remission. However, poor seizure control and developmental delay are associated with abnormal EEG background and characteristics, as well as neuroimaging abnormalities. The management of infantile-onset epilepsies may necessitate substantial resources and precise interventions to enhance overall outcomes.

Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms

Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalography (EEG) recordings of the frontal, temporal, and occipital scalp regions. This study included 38 patients with frontal (n = 15), temporal (n = 13), and occipital (n = 10) IEDs and 232 controls without IEDs from a single tertiary center. All the EEG recordings were segmented into 1.5-s epochs and fed into 1- or 2-dimensional convolutional neural networks to construct binary classification models to detect IEDs in each focal region and multiclass classification models to categorize IEDs into frontal, temporal, and occipital regions. The binary classification models exhibited accuracies of 79.3-86.4%, 93.3-94.2%, and 95.5-97.2% for frontal, temporal, and occipital IEDs, respectively. The three- and four-class models exhibited accuracies of 87.0-88.7% and 74.6-74.9%, respectively, with temporal, occipital, and non-IEDs F1-scores of 89.9-92.3%, 84.9-90.6%, and 84.3-86.0%; and 86.6-86.7%, 86.8-87.2%, and 67.8-69.2% for the three- and four-class (frontal, 50.3-58.2%) models, respectively. The deep learning-based models could help enhance EEG interpretation. Although they performed well, the resolution of region-specific focal IED misinterpretations and further model improvement are needed.

EEG Findings Enhance the Yield of Epilepsy Gene Panel Testing in Children

BACKGROUND

Epilepsies with encephalopathy are important to promptly and thoroughly evaluate because of their associated refractory nature and potential for neurologic regression.

METHODS

The results of epilepsy gene panel testing were obtained for 109 pediatric patients at Nemours Children's Center in Jacksonville, Florida, from 04/2019 to 02/2021. Results were obtained from Invitae TM via the offered epilepsy gene panel test and Behind the Seizure program. The EEGs for each patient were independently analyzed and categorized based on the presence or absence of features consistent with an epileptogenic encephalopathy.

RESULTS

EEG characterization and identification of patients with epileptogenic encephalopathy in our study was helpful in identifying patients, with an 80% yield on epilepsy gene panel testing.

CONCLUSIONS

EEG characterization can be incorporated into the evaluation of patients with epilepsy in order to optimize the selection of patients who are likely to benefit from an epilepsy gene panel test.

Advances in genetic testing and optimization of clinical management in children and adults with epilepsy

Introduction: Epileptic disorders are a heterogeneous group of medical conditions with epilepsy as the common denominator. Genetic causes, electro-clinical features, and management significantly vary according to the specific condition.Areas covered: Relevant diagnostic advances have been achieved thanks to the advent of Next Generation Sequencing (NGS)-based molecular techniques. These revolutionary tools allow to sequence all coding (whole exome sequencing, WES) and non-coding (whole genome sequencing, WGS) regions of human genome, with a potentially huge impact on patient care and scientific research.Expert opinion: The application of these tests in children and adults with epilepsy has led to the identification of new causative genes, widening the knowledge on the pathophysiology of epilepsy and resulting in therapeutic implications. This review will explore the most recent advancements in genetic testing and provide up-to-date approaches for the choice of the correct test in patients with epilepsy.

Clinical and Electroencephalographic Characteristics of Infantile-Onset Epilepsies Caused by Genetic Mutations

OBJECTIVES

To determine whether certain characteristic electroencephalography (EEG) features are indicative of a genetic cause in early-life epilepsy.

STUDY DESIGN

We enrolled a total of 100 patients with infantile-onset (<3 years) epilepsy due to known genetic cause (n = 50) and nongenetic cause (acquired, structural, or unknown, n = 50). The genetic group was classified into synaptopathies, channelopathies, mTOR (mammalian target of rapamycin)-opathies, and chromosomal abnormalities. The nongenetic group included epilepsy of unknown cause and structural abnormalities such as brain tumor, focal cortical dysplasia and encephalomalacia. The clinical features, magnetic resonance imaging, and video EEG obtained before 3 years of age and again at follow-up were reviewed. Specifically, the background rhythms and patterns of interictal epileptiform discharges were analyzed to define the EEG characteristics.

RESULTS

The genetic group was more likely to have seizure recurrence beyond infancy and significant developmental delay (P <.01). The genetic and nongenetic groups showed different EEG patterns in the initial EEGs that persisted in follow-up EEGs. Diffuse slowing with pleomorphic focal/multifocal epileptiform discharges were present more often in the genetic (86%) compared with the nongenetic group (20%) in the initial EEGs (P <.01). The last available follow-up EEG features were similar (81% in genetic versus 17% in nongenetic) to the EEG performed prior to 3 years of age.

CONCLUSIONS

Our findings suggest a simple guide for genetic screening in children with early-onset epilepsy. Genetic testing may be indicated and useful in infants with delayed development, no obvious cause, and significant EEG background slowing with pleomorphic focal or multifocal epileptiform discharges.