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Overview

Seizure Prediction from EEG

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Epilepsy is the second most neurological disorders, which 0.6 to 0.8% of people in the world suffer from.  Approximately 75% of the patients with epilepsy achieve partial or sufficient control over seizures from medication or resective surgery.  However, the remaining 25% of the patients do not have any treatment currently available.  If there is a way to predict occurrence of a seizure, it could sufficiently enhance the therapeutic possibilities, so it would lead the better quality of life to patients.

The general goal of this project is to propose a patient-specific algorithm, which can predict occurrences of an epileptic seizure in advance.  Specifically, this project intends to develop an algorithm to classify EEG (electroencephalogram) signals before a seizure onset from those during ordinary conditions with high sensitivity and a low rate of false positive.

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