EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

被引：53

作者：

Ahmadi N. ^{[1
]}

Pei Y. ^{[1
]}

Carrette E. ^{[2
]}

Aldenkamp A.P. ^{[3
]}

Pechenizkiy M. ^{[1
]}

机构：

[1] Department of Mathematics and Computer Science, Eindhoven University of Technology, TU/e, P.O.Box: 513, Eindhoven, 5600MB, NL

[2] Neurology Department, Ghent University Hospital, Ghent

[3] Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven

来源：

Brain Informatics | 2020年 / 7卷 / 01期

关键词：

Classification; EEG microstate; Epilepsy; Functional network; PNES;

D O I：

10.1186/s40708-020-00107-z

中图分类号：

学科分类号：

摘要：

Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined with EEG recording which is quite expensive and time-consuming. In this paper using short-term EEG data, the classification of epilepsy and PNES subjects is analyzed based on signal, functional network and EEG microstate features. Our results showed that the beta-band is the most useful EEG frequency sub-band as it performs best for classifying subjects. Also the results depicted that when the coverage feature of the EEG microstate analysis is calculated in beta-band, the classification shows fairly high accuracy and precision. Hence, the beta-band and the coverage are the most important features for classification of epilepsy and PNES patients. © 2020, The Author(s).

引用

共 59 条

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