Differentiating between psychogenic nonepileptic seizures and epilepsy based on common spatial pattern of weighted EEG resting networks.
Discriminating psychogenic nonepileptic seizures (PNES) from epilepsy is challenging, and a reliable and automatic classification remains elusive. In this study, we develop an approach for discriminating between PNES and epilepsy using the common spatial pattern extracted from the brain network topology (SPN). The study reveals that 92% accuracy, 100% sensitivity, and 80% specificity were reached for the classification between PNES and focal epilepsy. The newly developed SPN of resting EEG may be a promising tool to mine implicit information that can be used to differentiate PNES from epilepsy.
Peng
P
Xu
Xiuchun
X
Xiong
Qing
Q
Xue
Peiyang
P
Li
Rui
R
Zhang
Zhenyu
Z
Wang
Pedro A
PA
Valdes-Sosa
Yuping
Y
Wang
Dezhong
D
Yao
D001931:Brain Mapping / Q000379:methods*; D016022:Case-Control Studies; D004569:Electroencephalography / Q000145:classification* / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology*; D006801:Humans; D012640:Seizures / Q000175:diagnosis / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine