Abstract
Predicting epileptic seizures would change the life of millions of people. This work presents the results of a large study involving 216 patients with long-term scalp (sEEG) and intracranial (iEEG) records. A high-dimensional features space is built using time series data of 6 channels and 22 features per channel Patient-specific predictors based on SVM are developed and evaluated in relation to sensitivity and false-prediction rate. A substantial number of seizures has been correctly predicted and a comparative study is made with relation to the choice of electrodes, localization lateralization and preictal time duration. For a set of patients the results may be considered of clinical relevance compared to an analytic random predictor.
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