A New Technique for Classification of Focal and Nonfocal Eeg Signals Using Higher-Order Spectra

Epilepsy is a neurological disorder characterized by epileptic seizures inside the human brain. An authentic localization of epileptogenic area will help the clinicians for a successful epilepsy surgery. The epileptogenic area can be characterized by the focal electroencephalogram (EEG) signals. Hence, in this article, a bispectrum-based approach is implemented to characterize the focal EEG signals. The highest twenty-five magnitudes of bispectrum from the principal domain are used as features. The locality sensitive discriminant analysis (LSDA), data reduction technique, is implemented to reduce the number of attributes. The ranked LSDA attributes are input to the support vector machine (SVM) classifier yielding 96.2% classification accuracy using the entire Bern Barcelona EEG database. Hence, the proposed technique can be employed to confirm the epileptogenic area for successful epilepsy surgery and can be employed in the community health care centers and hospitals.