EEG microstate features for schizophrenia classification.

Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. Howev...

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Autores principales:	Kyungwon Kim, Nguyen Thanh Duc, Min Choi, Boreom Lee
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f4c7fbd839b477da1aa1840014fc6d9
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spelling	oai:doaj.org-article:2f4c7fbd839b477da1aa1840014fc6d92021-11-25T06:19:14ZEEG microstate features for schizophrenia classification.1932-620310.1371/journal.pone.0251842https://doaj.org/article/2f4c7fbd839b477da1aa1840014fc6d92021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251842https://doaj.org/toc/1932-6203Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.Kyungwon KimNguyen Thanh DucMin ChoiBoreom LeePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251842 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kyungwon Kim Nguyen Thanh Duc Min Choi Boreom Lee EEG microstate features for schizophrenia classification.
description	Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.
format	article
author	Kyungwon Kim Nguyen Thanh Duc Min Choi Boreom Lee
author_facet	Kyungwon Kim Nguyen Thanh Duc Min Choi Boreom Lee
author_sort	Kyungwon Kim
title	EEG microstate features for schizophrenia classification.
title_short	EEG microstate features for schizophrenia classification.
title_full	EEG microstate features for schizophrenia classification.
title_fullStr	EEG microstate features for schizophrenia classification.
title_full_unstemmed	EEG microstate features for schizophrenia classification.
title_sort	eeg microstate features for schizophrenia classification.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/2f4c7fbd839b477da1aa1840014fc6d9
work_keys_str_mv	AT kyungwonkim eegmicrostatefeaturesforschizophreniaclassification AT nguyenthanhduc eegmicrostatefeaturesforschizophreniaclassification AT minchoi eegmicrostatefeaturesforschizophreniaclassification AT boreomlee eegmicrostatefeaturesforschizophreniaclassification
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EEG microstate features for schizophrenia classification.

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