EEG microstate features for schizophrenia classification

• Published in: PloS one Vol. 16; no. 5; p. e0251842
• Main Authors: Kim, Kyungwon, Duc, Nguyen Thanh, Choi, Min, Lee, Boreom
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.05.2021; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Analysis; Biology and Life Sciences; Brain - diagnostic imaging; Brain - physiopathology; Brain Mapping - methods; Brain research; Classification; Cognitive ability; Datasets; Dependent variables; Diagnosis; EEG; Electroencephalography; Engineering; Evaluation; Female; Hearing loss; Humans; Illnesses; Independent variables; Information processing; Insomnia; Learning algorithms; Machine Learning; Male; Medicine and Health Sciences; Mental disorders; Multivariate Analysis; Nervous system; Neuroimaging; Neurosciences; Physical exercise; Physical Sciences; Reliability analysis; Research and Analysis Methods; Schizophrenia; Schizophrenia - classification; Schizophrenia - diagnostic imaging; Schizophrenia - physiopathology; Signal Processing, Computer-Assisted; Sleep disorders; Young Adult; United States; Canada; Montreal Quebec Canada; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0251842

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.