Probabilistic prediction of Epileptic Seizures using SVM.

In this paper, an algorithm based on the linear Support Vector Machine (SVM) tool was proposed to classify intracranial electroencephalography (iEEG) signals as ictal or interictal to perform human seizure prediction, efficiently. Various univariate linear measures were extracted, and the developed classifier performed adequately well with numerous performance metrics, especially the dataset was suffering from a significant imbalanced class, with the majority of samples representing non-seizure events. The proposed tool was indeed able to forecast accurately such rare events, seizures, from a large set of EEG dataset. In fact, our model can predict some seizures with up to 0.4 probability and about 30-40 minutes in advance. The proposed work employed intracranial EEG recordings of 6 patients in the Freiburg EEG database, totalling trained and tested on 34 seizures of 140-hour-long. It exhibits a sensitivity of 78% and specificity of 100% employing a 2-second-long window with 10-fold cross-validation.