The breakthrough in detecting early onset of refractory epilepsy in children may lead to effective treatment using non-pharmacological therapies.

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 anti-epileptic drugs (AED).

The team developed an efficient, automated and quantitative approach towards the early prediction of refractory epilepsy based on EEG classification analysis.

In the study, a set of 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.

"The early identification of patients whose seizures are refractory to AED would allow them to receive alternative therapies at an appropriate time," researchers concluded.

The paper appeared in the International Journal of Neural Systems (IJNS).

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