TITLE:
Neuropathology Classifier Based on Higher Order Spectra
AUTHORS:
Cesar Seijas, Antonino Caralli, Sergio Villazana
KEYWORDS:
Higher Order Spectra; Classification; Support Vector Machines; EEG; Epilepsy
JOURNAL NAME:
Journal of Computer and Communications,
Vol.1 No.4,
October
29,
2013
ABSTRACT: Epilepsy is the most common
neuropathology. Statistical
studies related to the disease reported that 20% - 25% of epileptic patients with occurrence of seizures were even under treatment with drugs. This article
presents a strategy for improved detection of the neuropathology, based on electroencephalogram
(EEG), using a classifier built with support vector machines (SVC). The SVC is designed
based on feature extraction of higher order spectra of time series derived from
the EEG applied to epileptic patients and control patients. As demonstrated in the
study presented, the EEG time series are highly nonlinear and non-Gaussian, therefore,
exhibit higher order spectra, which are extracted features that improve the accuracy
in the performance of SVC. The results of this study suggest the development of
highly accurate computational tools for the diagnosis of this dreaded neuropathology.