Early prediction of encephalopathic transformation in children with benign epilepsy with centro-temporal spikes

Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy

PURPOSE

Some individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data.

METHODS

The study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms.

RESULTS

Among the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features.

CONCLUSIONS

Considering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.

The Prevalence and Risk Factors of Electrical Status Epilepticus During Slow-Wave Sleep in Self-Limited Epilepsy With Centrotemporal Spikes

Objective. To investigate the prevalence and risk factors for electrical status epilepticus during slow-wave sleep (ESES) in patients with self-limited epilepsy with centrotemporal spikes (SeLECTS). Methods. The clinical and follow-up data of children with SeLECTS were collected between 2017 and 2021. Patients were divided into typical ESES, atypical ESES, and non-ESES groups according to spike-wave indices (SWI). Clinical and electroencephalography characteristics were retrospectively analyzed. Logistic regression was used to identify risk factors for ESES. Results. A total of 95 patients with SeLECTS were enrolled. Seven patients (7.4%) developed typical ESES, 30 (31.6%) developed atypical ESES, 25 (26.3%) developed ESES at the first visit, and 12 (12.6%) developed ESES during treatment and follow-up. Multivariate logistic regression analysis showed that the risk factors for SeLECTS combined with ESES were Rolandic double or multiple spikes (OR = 8.626, 95% CI: 2.644-28.147, P < .001) and Rolandic slow waves (OR = 53.550, 95% CI: 6.339-452.368, P < .001). There were no significant differences in seizure characteristics, electroencephalogram (EEG) findings, or cognitive impairment between the atypical and typical ESES groups. Conclusion. More than one-third of the SeLECTS patients combined with ESES. Both atypical and typical ESES scores can affect cognitive function. On electroencephalography, interictal Rolandic double/multiple spikes and slow-wave abnormalities may indicate SeLECTS with ESES.

Continuous Spike-Waves during Slow Sleep Today: An Update

In the context of childhood epilepsy, the concept of continuous spike-waves during slow sleep (CSWS) includes several childhood-onset heterogeneous conditions that share electroencephalograms (EEGs) characterized by a high frequency of paroxysmal abnormalities during sleep, which have negative effects on the cognitive development and behavior of the child. These negative effects may have the characteristics of a clear regression or of a slowdown in development. Seizures are very often present, but not constantly. The above makes it clear why CSWS have been included in epileptic encephalopathies, in which, by definition, frequent EEG paroxysmal abnormalities have an unfavorable impact on cognitive functions, including socio-communicative skills, causing autistic features, even regardless of the presence of clinically overt seizures. Although several decades have passed since the original descriptions of the electroclinical condition of CSWS, there are still many areas that are little-known and deserve to be further studied, including the EEG diagnostic criteria, the most effective electrophysiological parameter for monitoring the role of the thalamus in CSWS pathogenesis, its long-term evolution, the nosographic location of Landau-Kleffner syndrome, standardized neuropsychological and behavioral assessments, and pharmacological and non-pharmacological therapies.