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.
Highlights
Schizophrenia is a mental illness whose fundamental nature is not fully understood
Our results suggest that microstate features reflect schizophrenia characteristics and show better classification performance than conventional EEG features
We provided evidence for usefulness of EEG microstate features in schizophrenia classification
Summary
Schizophrenia is a mental illness whose fundamental nature is not fully understood. Emil Kraepelin [1] and Eugen Bleuler [2] developed and advocated conceptualizations of schizophrenia, they alone do not explain its various manifestations. Several studies since [3, 4] have documented changes in cognitive function as core symptoms of schizophrenia, and with advances in neuroscience modalities, many researchers have attempted to reveal clinical. EEG microstate features for schizophrenia classification symptoms of schizophrenia objectively. Their work has improved understanding of the illness, thereby enabling causes to be identified and treatments to be developed. Objective research of schizophrenia continues to be very important
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.