Multi-feature characterization of epileptic activity for construction of an automated internet-based annotated classification.

Continuous monitoring of EEG is essential for the neurologist to detect the epileptic seizures that occur at various intervals. Since large volume of data need to be analyzed, visual analysis has been proven to be time consuming and subsequently automated detection techniques have gained importance in the recent years. For the biomedical research community, the major challenge lies in providing a solution to neurologists in terms of diagnosis and EEG database management. This paper discusses the automated detection of epileptic seizure using frequency domain and entropy parameters which helps in the construction of epileptic database for handling EEG data. Experimental study indicates that the suggested mode of operation can be used for internet based framework which contains pure epileptic patterns in the server. This can be retrieved and analyzed for detection and annotation of epileptic spikes in extensive EEG recordings.