EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features
Abstract 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 electroence...
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doaj-c92e42b1d50b45f4b3eb305924e77c8e2020-11-25T03:46:14ZengSpringerOpenBrain Informatics2198-40182198-40262020-05-017112210.1186/s40708-020-00107-zEEG-based classification of epilepsy and PNES: EEG microstate and functional brain network featuresNegar Ahmadi0Yulong Pei1Evelien Carrette2Albert P. Aldenkamp3Mykola Pechenizkiy4Department of Mathematics and Computer Science, Eindhoven University of TechnologyDepartment of Mathematics and Computer Science, Eindhoven University of TechnologyNeurology Department, Ghent University HospitalDepartment of Electrical Engineering, Eindhoven University of TechnologyDepartment of Mathematics and Computer Science, Eindhoven University of TechnologyAbstract 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.http://link.springer.com/article/10.1186/s40708-020-00107-zEEG microstateFunctional networkClassificationEpilepsyPNES |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Negar Ahmadi Yulong Pei Evelien Carrette Albert P. Aldenkamp Mykola Pechenizkiy |
spellingShingle |
Negar Ahmadi Yulong Pei Evelien Carrette Albert P. Aldenkamp Mykola Pechenizkiy EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features Brain Informatics EEG microstate Functional network Classification Epilepsy PNES |
author_facet |
Negar Ahmadi Yulong Pei Evelien Carrette Albert P. Aldenkamp Mykola Pechenizkiy |
author_sort |
Negar Ahmadi |
title |
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features |
title_short |
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features |
title_full |
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features |
title_fullStr |
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features |
title_full_unstemmed |
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features |
title_sort |
eeg-based classification of epilepsy and pnes: eeg microstate and functional brain network features |
publisher |
SpringerOpen |
series |
Brain Informatics |
issn |
2198-4018 2198-4026 |
publishDate |
2020-05-01 |
description |
Abstract 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. |
topic |
EEG microstate Functional network Classification Epilepsy PNES |
url |
http://link.springer.com/article/10.1186/s40708-020-00107-z |
work_keys_str_mv |
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