Abstract
A comprehensive insight into the epileptiform discharges at the time of seizure onset can aid neurophysiologists in the diagnosis and treatment of epileptic seizures. Visual analysis of seizure onset patterns is often a complex and tedious task. These problems suggest the development of automated seizure onset detection systems. The present research work is oriented for automatic detection of epileptic seizures at the onset using statistical measures. A quadratic classifier with fourfold cross-validation is used to demarcate the seizure and non-seizure activity. The algorithm is evaluated for 24 patients from the CHB MIT scalp EEG database. Classifier performance is assessed in terms of sensitivity, specificity, accuracy, and latency.
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