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StudiuEpilepsieÎncredere înaltă22.06.2026

Late-onset epilepsy with cognitive symptoms: Clinical and neuroimaging comparison with probable Alzheimer's disease

This study compares cognitive, neuropsychological, and neuroimaging profiles of 26 late-onset epilepsy (LOE) patients with cognitive symptoms to 26 probable Alzheimer's disease (AD) patients, using comprehensive assessments including ACE-III cognition testing and 18F-FDG PET-CT imaging. Results showed that while LOE patients exhibited significant cognitive impairment (18% with dementia-range deficits), they demonstrated different cognitive profiles than AD patients, with better naming, memory, and visuospatial abilities but comparable deficits in executive function and attention.

StudiuEpilepsieÎncredere bună21.06.2026

Prediction of Epileptic Seizure Using Deep Learning Techniques

This study proposes a deep learning approach for predicting epileptic seizures using EEG signal analysis, employing convolutional neural networks for feature extraction and Bi-LSTM for classification. The method achieved 99.61% accuracy and aims to improve patient quality of life through early seizure detection and timely intervention.

StudiuEpilepsieÎncredere înaltă21.06.2026

Systematic Literature Review of Humanistic and Economic Burden of Focal Epilepsy and Primary Generalized Tonic-Clonic Seizures in Adults

This systematic review analyzed 136 studies to assess the humanistic and economic impact of focal epilepsy and primary generalized tonic-clonic seizures in adults, examining patient outcomes, quality of life, productivity loss, and caregiver burden. Findings demonstrate that epilepsy, particularly focal epilepsy, imposes substantial burden including poor work productivity, increased sick days, early retirement, and significant caregiver costs, with greater impact associated with high seizure frequency and medication resistance.

StudiuEpilepsieÎncredere înaltă21.06.2026

Machine Learning Models for Predicting Anti-Seizure Medication Response in Epilepsy: A Systematic Review

This systematic review of 37 studies examines how machine learning models can predict individual patient responses to anti-seizure medications by analyzing clinical data, brain imaging, EEG, and genetic information. While some models show promising accuracy, results vary widely, and the field faces significant limitations related to data quality and quantity that require further research and standardization.

StudiuEpilepsieÎncredere bună21.06.2026

Switching from carbamazepine to lacosamide improves elevated gamma-glutamyl transpeptidase levels in focal epilepsy patients

A clinical study of 12 adult epilepsy patients with elevated liver enzyme (GGT) levels found that switching from carbamazepine to lacosamide resulted in significant reduction in GGT levels and normalization in most patients. The medication switch was conducted abruptly without gradual cross-titration and did not result in increased seizure incidence over one month of follow-up.

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