Detection of seizure using EEG Signals by Supervised Learning Algorithms

Epileptic seizure can be detected by many ways but EEG signal prove to be the most important marker. Since EEG signal requires a strenuous effort to go through pages of recorded signal. Automatic seizure detection can be done by extracting features from the EEG signals and then feeding them to the supervised learning algorithms for classification and prediction. In this paper the features that are chosen are mean, standard deviation, skewness, kurtosis, interquartile range and mean absolute deviation. A comparative study of SVM and GRNN are done in this work and GRNN proves to be accurate for seizure detection applications.