Title :
Differentiating Between Psychogenic Nonepileptic Seizures and Epilepsy Based on Common Spatial Pattern of Weighted EEG Resting Networks
Author :
Peng Xu ; Xiuchun Xiong ; Qing Xue ; Peiyang Li ; Rui Zhang ; Zhenyu Wang ; Valdes-Sosa, Pedro A. ; Yuping Wang ; Dezhong Yao
Author_Institution :
Key Lab. for NeuroInformation of the Minist. of Educ., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
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.
Keywords :
electroencephalography; feature extraction; medical disorders; medical signal processing; neurophysiology; signal classification; topology; SPN; brain network topology; classification accuracy; classification sensitivity; classification specificity; common spatial pattern extraction; focal epilepsy classification; implicit information mining; psychogenic nonepileptic seizure classification; reliable automatic PNES classification; weighted EEG resting networks; Coherence; Educational institutions; Electrodes; Electroencephalography; Epilepsy; Feature extraction; Network topology; Brain network; common spatial pattern of brain network topology; psychogenic nonepileptic seizures (PNES); resting scalp EEG;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2014.2305159
