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Objective: Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo‐electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high‐frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. Methods: In 50 patients we analyzed 30 min of SEEG recorded during non–rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1–500 Hz); the second model used low‐frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision‐recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed‐rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. Results: The AUPRC score of the random chance classifier was.098. The models (broad‐band, low‐frequency, high‐frequency) achieved median AUPRCs of.608,.582, and.522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. Significance: Low‐frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines. [ABSTRACT FROM AUTHOR]

Hyponatremia is a typical side effect of antiseizure drugs from the dibenzazepine family. The study investigated the prevalence of hyponatremia in patients with epilepsy who were treated with eslicarbazepine. We aimed to determine the prevalence of hyponatremia, reveal the factors leading to the discontinuation of treatment, and identify possible risk factors for the development of hyponatremia including the dose dependency. The medical records of 164 patients with epilepsy taking eslicarbazepine in our center were analyzed. The overall prevalence of hyponatremia was 30.5%. The prevalence of mild hyponatremia, seen in 14%–20% of patients, was not dose dependent. The prevalence of moderate and severe hyponatremia was significantly dose dependent. The severity of hyponatremia was significantly dose dependent. Severe hyponatremia was found in 6.1% of patients. Hyponatremia was asymptomatic in the majority of cases, and in 48% did not require any management. Hyponatremia was the reason for discontinuation in 6.2% of patients. The major risk factor for developing hyponatremia was older age. The study shows that eslicarbazepine‐induced hyponatremia is usually mild and asymptomatic. It usually does not require any management and seldom leads to treatment discontinuation. Hyponatremia is dose dependent. Another major risk for developing hyponatremia (besides dose) is older age. [ABSTRACT FROM AUTHOR]

Objective: Focal cortical dysplasia (FCD), hippocampal sclerosis (HS), nonspecific gliosis (NG), and normal tissue (NT) comprise the majority of histopathological results of surgically treated drug‐resistant epilepsy patients. Epileptic spikes, high‐frequency oscillations (HFOs), and connectivity measures are valuable biomarkers of epileptogenicity. The question remains whether they could also be utilized for preresective differentiation of the underlying brain pathology. This study explored spikes and HFOs together with functional connectivity in various epileptogenic pathologies. Methods: Interictal awake stereoelectroencephalographic recordings of 33 patients with focal drug‐resistant epilepsy with seizure‐free postoperative outcomes were analyzed (15 FCD, 8 HS, 6 NT, and 4 NG). Interictal spikes and HFOs were automatically identified in the channels contained in the overlap of seizure onset zone and resected tissue. Functional connectivity measures (relative entropy, linear correlation, cross‐correlation, and phase consistency) were computed for neighboring electrode pairs. Results: Statistically significant differences were found between the individual pathologies in HFO rates, spikes, and their characteristics, together with functional connectivity measures, with the highest values in the case of HS and NG/NT. A model to predict brain pathology based on all interictal measures achieved up to 84.0% prediction accuracy. Significance: The electrophysiological profile of the various epileptogenic lesions in epilepsy surgery patients was analyzed. Based on this profile, a predictive model was developed. This model offers excellent potential to identify the nature of the underlying lesion prior to resection. If validated, this model may be particularly valuable for counseling patients, as depending on the lesion type, different outcomes are achieved after epilepsy surgery. [ABSTRACT FROM AUTHOR]