Spotting Refractory Epilepsy Early With Scalp EEG

Scientists have developed an automated and quantitative method of diagnosing epilepsy in children based on scalp EEG.

AsianScientist (Sep. 16, 2014) – It is now easier to detect and diagnose epilepsy in children, thanks to a study by medical doctors and scientists in Taiwan. This research has been published in the International Journal of Neural Systems.

Epilepsy is the most common chronic disease in pediatric neurology, with about 0.5-1 percent of children developing epilepsy during their lifetime. A further 30-40 percent 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.

Using a new electroencephalography (EEG) analytical method, researchers from Kaoshiung Medical University have successfully developed a tool to detect certain EEG features often present in children with idiopathic epilepsy.

EEG analysis is widely employed to investigate brain disorders and to study brain electrical activity. In the study, a set of artifact-free EEG segments was acquired from the EEG recordings of patients belonging to two classes of epilepsy: well-controlled and refractory. To search for significantly discriminative EEG features and to reduce computational costs, a statistical approach involving global parametric features was adopted across EEG channels as well as over time. A gain ratio-based feature selection was then performed.

Based on their EEG method, the researchers were able to develop a diagnostic tool, using a support vector machine classification model to discriminate between well-controlled idiopathic epilepsy and refractory idiopathic epilepsy. The study also found that refractory patients have a higher risk of seizure attacks than well-controlled patients.

The authors say that further research with more diversity in terms of pediatric and adult participants will help to expand on the tool’s reliability and generalization.

The article can be found at: Lin et al. (2014) Early Prediction of Medication Refractoriness in Children with Idiopathic Epilepsy Based On Scalp EEG Analysis.


Source: World Scientific Publishing Company.
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