Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity (II): A combinatorial optimization approach for electrode selection

In this sequel to our previous work [Rosso OA, Mendes A, Rostas JA, Hunter M, Moscato P. Distinguishing childhood absence epilepsy patients from controls by the analysis of their background brain electrical activity. J. Neurosci. Methods 2009;177:461–68], we extend the analysis of background electroencephalography (EEG), recorded with scalp electrodes in a clinical setting, in children with childhood absence epilepsy (CAE) and control individuals. The same set of individuals was considered—five CAE patients, all right-handed females and aged 6–8 years. The EEG was obtained using bipolar connections from a standard 10–20 electrode placement. The functional activity between electrodes was evaluated using a wavelet decomposition in conjunction with the Wootters distance. In the previous study, a Kruskal–Wallis statistical test was used to select the pairs of electrodes with differentiated behavior between CAE and control samples (classes). In this contribution, we present the results for a combinatorial optimization approach to select the pairs of electrodes. The new method produces a better separation between the classes, and at the same time uses a smaller number of features (pairs of electrodes). It managed to almost halve the number of features and also improves the separation between the CAE and control samples. The new results strengthen the hypothesis that mostly fronto-central electrodes carry useful information and patterns that can help to discriminate CAE cases from controls. Finally, we provide a comprehensive set of tests and in-depth explanation of the method and results.