Automated epileptic seizure detection using relevant features in support vector machines

Automatic seizure detection is very essential for monitoring and rehabilitation of epilepsy patients and will open up new treatment possibilities for saving the lives of epileptic patients. In recent years, many algorithms for the automatic seizure detection have been proposed and applied, in which Support vector machines proved to be a robust machine learning algorithm. The purpose of this study is to compute relevant EEG features and apply a feature selection algorithm to select an optimum set of features for use in a classification scheme for epileptic seizure detection. Thus S VM will thereby yield a better accuracy compared to other algorithms. Effective features such as energy, relative amplitude, standard deviation, coefficient of variation, fluctuation index etc are selected and then these features are fed into the support vector machine for training and classification. This algorithm makes use of Radial Basis Function Kernels for training data and thus obtains more accurate results.