A comparative study of intracranial EEG files using nonlinear classification methods.

Abstract

This study is a comparative evaluation of nonlinear classification methods with a focus on nonlinear decision functions and the standard method of support vector machines for seizure detection. These nonlinear classification methods are used on key features that were extracted on subdural EEG data after a thorough evaluation of all the frequency bands from 1 to 44 Hz. The sensitivity, specificity, and accuracy of seizure detection reveal that the gamma frequencies (36-44 Hz) are most suitable for detecting seizure files using a unique 2D decisional plane. We evaluated 157 intracranial EEG files from 14 patients by calculating the spectral power using nonoverlapping 1-s windows on different frequency bands. A key finding is in establishing a 2D decision plane, where duration of the seizure is used as the first dimension (x coordinate) and the maximum of the gamma frequency components is used as the second dimension (y coordinate). Within this 2D plane, the best results were observed when the nonlinearity degree is three for the proposed nonlinear decision functions, with a sensitivity of 96.3%, a specificity of 96.8%, and accuracy of 96.7%.