65 million people around the world today suffer from epilepsy, a condition of the brain that may trigger an uncontrollable seizure at any time, often for no known reason. A seizure is a disruption of the electrical communication between neurons, and someone is said to have epilepsy if they experience two or more unprovoked seizures separated by at least 24 hours. Epilepsy is the most common chronic disease in pediatric neurology, with approximately 0.5-1% of children developing epilepsy during their lifetime. A further 30-40% of epileptic children develop refractory epilepsy, a particular type of epilepsy that cannot be managed by antiepileptic drugs (AED). Regardless of etiology, children with refractory epilepsy are invariably exposed to a variety of physical, psychological, and social morbidities. Patients whose seizures are difficult to control could benefit from non-pharmacological therapies, including surgery, deep brain stimulation, and ketogenic diets. Therefore, the early identification of patients whose seizures are refractory to AED would allow them to receive alternative therapies at an appropriate time. Despite idiopathic etiology being a significant predictor of a lower risk of refractory epilepsy, a subset of patients with idiopathic epilepsy might still be refractory to medical treatment. Using a new electroencephalography (EEG) analytical method, a team of medical doctors and scientists in Taiwan has successfully developed a tool to detect certain EEG features often present in children with idiopathic epilepsy. The team developed an efficient, automated, and quantitative approach towards the early prediction of refractory idiopathic epilepsy based on EEG classification analysis. EEG analysis is widely employed to investigate brain disorders and to study brain electrical activity.
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