A decision support framework for the discrimination of children with controlled epilepsy based on EEG analysis

Abstract

Background: In this work we consider hidden signs (biomarkers) in ongoing EEG activity expressing epileptic

tendency, for otherwise normal brain operation. More specifically, this study considers children with controlled

epilepsy where only a few seizures without complications were noted before starting medication and who showed no

clinical or electrophysiological signs of brain dysfunction. We compare EEG recordings from controlled epileptic

children with age-matched control children under two different operations, an eyes closed rest condition and a

mathematical task. The aim of this study is to develop reliable techniques for the extraction of biomarkers from EEG

that indicate the presence of minor neurophysiological signs in cases where no clinical or significant EEG abnormalities

are observed.

Methods: We compare two different approaches for localizing activity differences and retrieving relevant information

for classifying the two groups. The first approach focuses on power spectrum analysis whereas the second approach

analyzes the functional coupling of cortical assemblies using linear synchronization techniques.

Results: Differences could be detected during the control (rest) task, but not on the more demanding mathematical

task. The spectral markers provide better diagnostic ability than their synchronization counterparts, even though a

combination (or fusion) of both is needed for efficient classification of subjects.

Conclusions: Based on these differences, the study proposes concrete biomarkers that can be used in a decision

support system for clinical validation. Fusion of selected biomarkers in the Theta and Alpha bands resulted in an

increase of the classification score up to 80% during the rest condition. No significant discrimination was achieved

during the performance of a mathematical subtraction task.