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Background: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analysis of electroencephalogram (EEG) signals have garnered widespread attention, particularly in the area of seizure recognition.

Methods: A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study.

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Introduction: This study investigated low-density scalp electrical source imaging of the ictal onset zone and interictal spike ripple high-frequency oscillation networks using source coherence maps in the pediatric epilepsy surgical workup. Intracranial monitoring, the gold standard for determining epileptogenic zones, has limited spatial sampling. Source coherence analysis presents a promising new non-invasive technique.

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Spike-wave-discharges (SWD) are the electrophysiological hallmark of absence epilepsy. SWD are generated in the thalamo-cortical network and a seizure onset zone was identified in the somatosensory cortex (S1). We have shown before that inhibition of the centromedian thalamic nucleus (CM) in GAERS rats resulted in a selective suppression of the spike component while rhythmic cortical 5-9 Hz oscillations remained present.

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The association between migraine and cognitive function has been studied during the last decade, however, this relationship is not well established. As migraine prevalence is highest between the ages of 30-40, aligning with some of our most productive years, we must understand cognitive changes within this disorder. Cognitive impairment potentially limits social and professional interactions, thus negatively impacting quality of life.

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Objective: The aim of this study was to describe the clinical features of contactin-associated protein-like 2 (CASPR2)-IgG-associated seizures.

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