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

Volume

61

Issue

6

fYear

2014

fDate

41791

Firstpage

1747

Lastpage

1755

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;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2014.2305159

Filename

6734688