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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| Published in | PloS one Vol. 16; no. 5; p. e0251842 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
14.05.2021
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0251842 |
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| Abstract | 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. |
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| AbstractList | 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. [...]it is suitable for studying complex cognitive functions. [...]EEG can provide unique information that is otherwise difficult to obtain using imaging modalities. The features based on those microstates show differences between patients with schizophrenia and other groups and allow interpretation from a neuroscience perspective. [...]four archetype microstates were used not only in schizophrenia [24, 37–39, 46] but also in general medical conditions, such as physical exercise [54], insomnia [55], hearing loss [56]. With multivariate analysis, we can simultaneously analyze multiple dependent and independent variables to improve reliability and validity. [...]multivariate analysis can utilize all microstate-feature information and identify new patterns to improve understanding [58, 59]. Machine-learning techniques (e.g., classification using kernel method) accomplish multivariate analyses that catalog distinct observations and allocate new observations to previously defined groups [60]. [...]by applying machine-learning-based algorithms to microstate features, we can distinguish between EEG recordings of patients diagnosed with schizophrenia and those of healthy (control) subjects and present a practical application. 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.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. [...]it is suitable for studying complex cognitive functions. [...]EEG can provide unique information that is otherwise difficult to obtain using imaging modalities. The features based on those microstates show differences between patients with schizophrenia and other groups and allow interpretation from a neuroscience perspective. [...]four archetype microstates were used not only in schizophrenia [24, 37–39, 46] but also in general medical conditions, such as physical exercise [54], insomnia [55], hearing loss [56]. With multivariate analysis, we can simultaneously analyze multiple dependent and independent variables to improve reliability and validity. [...]multivariate analysis can utilize all microstate-feature information and identify new patterns to improve understanding [58, 59]. Machine-learning techniques (e.g., classification using kernel method) accomplish multivariate analyses that catalog distinct observations and allocate new observations to previously defined groups [60]. [...]by applying machine-learning-based algorithms to microstate features, we can distinguish between EEG recordings of patients diagnosed with schizophrenia and those of healthy (control) subjects and present a practical application. |
| Audience | Academic |
| Author | Choi, Min Duc, Nguyen Thanh Kim, Kyungwon Lee, Boreom |
| AuthorAffiliation | 1 Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea 3 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada 4 McConnel Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada 2 Department of Psychiatry and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea 5 Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada McLean Hospital, UNITED STATES |
| AuthorAffiliation_xml | – name: 4 McConnel Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada – name: 5 Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada – name: 3 Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada – name: McLean Hospital, UNITED STATES – name: 2 Department of Psychiatry and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea – name: 1 Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea |
| Author_xml | – sequence: 1 givenname: Kyungwon surname: Kim fullname: Kim, Kyungwon – sequence: 2 givenname: Nguyen Thanh surname: Duc fullname: Duc, Nguyen Thanh – sequence: 3 givenname: Min surname: Choi fullname: Choi, Min – sequence: 4 givenname: Boreom orcidid: 0000-0002-7233-5833 surname: Lee fullname: Lee, Boreom |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33989352$$D View this record in MEDLINE/PubMed |
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| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco – name: San Francisco, CA USA |
| PublicationTitle | PloS one |
| PublicationTitleAlternate | PLoS One |
| PublicationYear | 2021 |
| Publisher | Public Library of Science Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science – name: Public Library of Science (PLoS) |
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| Snippet | Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states.... [...]it is suitable for studying complex cognitive functions. [...]EEG can provide unique information that is otherwise difficult to obtain using imaging... [...]it is suitable for studying complex cognitive functions. [...]EEG can provide unique information that is otherwise difficult to obtain using imaging... |
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| Title | EEG microstate features for schizophrenia classification |
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