A decision support framework for the discrimination of children with controlled epilepsy based on EEG analysis.
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.
Biomedical Informatics Lab, Institute of Computer Science, Foundation for Research and Technology, Heraklion, Greece. sakkalis@ics.forth.gr
Vangelis
V
Sakkalis
Tracey
T
Cassar
Michalis
M
Zervakis
Ciprian D
CD
Giurcaneanu
Cristin
C
Bigan
Sifis
S
Micheloyannis
Kenneth P
KP
Camilleri
Simon G
SG
Fabri
Eleni
E
Karakonstantaki
Kostas
K
Michalopoulos
D000293:Adolescent; D002648:Child; D020000:Decision Support Systems, Clinical; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted