Neuropathology Classifier Based on Higher Order Spectra

DOI: 10.4236/jcc.2013.14005   PDF   HTML   XML   3,530 Downloads   5,417 Views   Citations

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.

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Seijas, C. , Caralli, A. and Villazana, S. (2013) Neuropathology Classifier Based on Higher Order Spectra. Journal of Computer and Communications, 1, 28-32. doi: 10.4236/jcc.2013.14005.

Conflicts of Interest

The authors declare no conflicts of interest.

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