Literature DB >> 25595562

Diagnostic utility of quantitative EEG in un-medicated schizophrenia.

Jun Won Kim1, Young Sik Lee2, Doug Hyun Han3, Kyung Joon Min3, Jaewon Lee4, Kounseok Lee5.   

Abstract

The aim of the current study was to evaluate the quantitative electroencephalography (QEEG) characteristics of patients with un-medicated schizophrenia (SPR) and to investigate the diagnostic utility of QEEG in assessing such patients during resting conditions. The subjects included 90 patients with schizophrenia and 90 normal controls. Spectral analysis was performed on the absolute power of all of the electrodes across five frequency bands following artifact removal. We conducted a repeated-measures ANOVA to examine group differences within the five frequency bands across several brain regions and receiver operator characteristic (ROC) analyses to examine the discrimination ability of each frequency band. Compared with controls, patients with schizophrenia showed increased delta and theta activity and decreased alpha 2 activity, particularly in the frontocentral area. There were no significant differences in the alpha 1 and beta activity. The ROC analysis performed on the delta frequency band generated the best result, with an overall classification accuracy of 62.2%. The results of this study confirmed the characteristics of the QEEG power in un-medicated schizophrenia patients compared with normal controls. These findings suggest that a resting EEG test can be a supportive tool for evaluating patients with schizophrenia.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Discrimination ability; Quantitative electroencephalography; Spectral analysis; Un-medicated schizophrenia

Mesh:

Year:  2015        PMID: 25595562     DOI: 10.1016/j.neulet.2014.12.064

Source DB:  PubMed          Journal:  Neurosci Lett        ISSN: 0304-3940            Impact factor:   3.046


  23 in total

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Review 3.  Aberrant Network Activity in Schizophrenia.

Authors:  Mark J Hunt; Nancy J Kopell; Roger D Traub; Miles A Whittington
Journal:  Trends Neurosci       Date:  2017-05-14       Impact factor: 13.837

4.  Automatic classification of schizophrenia patients using resting-state EEG signals.

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Journal:  Phys Eng Sci Med       Date:  2021-08-09

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Authors:  Yingyi Zhang; Alexandra Geyfman; Brian Coffman; Kathryn Gill; Fabio Ferrarelli
Journal:  Schizophr Res       Date:  2021-01-09       Impact factor: 4.939

6.  Separating scale-free and oscillatory components of neural activity in schizophrenia.

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Journal:  Clin EEG Neurosci       Date:  2020-09-25       Impact factor: 1.843

8.  An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data.

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Journal:  Sci Rep       Date:  2021-07-19       Impact factor: 4.379

9.  Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity.

Authors:  Zongya Zhao; Jun Li; Yanxiang Niu; Chang Wang; Junqiang Zhao; Qingli Yuan; Qiongqiong Ren; Yongtao Xu; Yi Yu
Journal:  Front Neurosci       Date:  2021-06-03       Impact factor: 4.677

10.  Automated detection of schizophrenia using optimal wavelet-based l 1 norm features extracted from single-channel EEG.

Authors:  Manish Sharma; U Rajendra Acharya
Journal:  Cogn Neurodyn       Date:  2021-01-15       Impact factor: 3.473

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