作者: Osvaldo A. Rosso , Alexandre Mendes , Regina Berretta , John A. Rostas , Mick Hunter
DOI: 10.1016/J.JNEUMETH.2009.04.028
关键词: Electroencephalography 、 Wavelet decomposition 、 Childhood absence epilepsy 、 Combinatorial optimization 、 Selection (genetic algorithm) 、 Statistical hypothesis testing 、 Brain electrical activity 、 Pattern recognition 、 Psychology 、 Artificial intelligence 、 Set (psychology)
摘要: 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 electroencephalography (EEG), recorded with scalp electrodes in a clinical setting, children (CAE) and control individuals. The same set individuals was considered-five CAE patients, all right-handed females aged 6-8 years. EEG obtained using bipolar connections standard 10-20 electrode placement. functional activity between evaluated wavelet decomposition conjunction Wootters distance. study, Kruskal-Wallis statistical test used select pairs differentiated behavior samples (classes). contribution, present results for combinatorial optimization approach electrodes. new method produces better separation classes, at time uses smaller number features (pairs electrodes). It managed almost halve also improves samples. strengthen hypothesis that mostly fronto-central carry useful information patterns can help discriminate cases controls. Finally, provide comprehensive tests in-depth explanation results.