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. 2010 Jun 2:7:24.
doi: 10.1186/1743-0003-7-24.

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

Vangelis Sakkalis  1 , Tracey Cassar, Michalis Zervakis, Ciprian D Giurcaneanu, Cristin Bigan, Sifis Micheloyannis, Kenneth P Camilleri, Simon G Fabri, Eleni Karakonstantaki, Kostas Michalopoulos
Affiliations

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

Vangelis Sakkalis et al. J Neuroeng Rehabil. .

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.

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Figures

Figure 1
Figure 1
Electrode montage consisting of 30 electrodes placed according to the 10/20 international electrode placement system.
Figure 2
Figure 2
Topographic maps showing the p-values of WT power differences between control and epilepsy subjects for Task 1 and Task 2. The black dots in each image represent the channel locations. Lower p-values are indicated in shades of blue while p-values close to the threshold of 0.1 are indicated in shades of red. Blank areas within each topographic map indicate that the features extracted from that particular lobe do not give significant differences between the two populations (p > 0.1).
Figure 3
Figure 3
Classification scores, Sensitivity and Specificity using WT features: Results for Task 1.
Figure 4
Figure 4
Classification scores, Sensitivity and Specificity using WT features: Results for Task 2.
Figure 5
Figure 5
Classification scores, Sensitivity and Specificity results using MS-COH and AR-COH features: Results for Task 1.
Figure 6
Figure 6
Classification scores, Sensitivity and Specificity using MS-COH and AR-COH features: Results for Task 2.
Figure 7
Figure 7
Averaged WT biomarkers across the 20 epileptic and 20 control subjects, for each frequency band and brain lobe considered.
See this image and copyright information in PMC

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