Abstract：
:Features from Electrovestibulography (EVestG) recordings have been used to classify and measure the severity of both persistent post-concussion syndrome (PCS) and major depressive disorder. Herein, we examined the effect of comorbid depression on the detection of persistent PCS using EVestG. To validate our previously developed EVestG classifier for PCS detection, the classifier was tested with a new blind dataset (N = 21). The unbiased accuracy for identifying the new PCS from controls was found to be >90%. Next, the PCS group (N = 59) was divided into three subgroups: PCS with no-depression (n = 18), PCS with mild-depression (n = 27) and PCS with moderate/severe-depression (n = 14). When moderate/severe depression was present, PCS classification accuracy dropped to 83%. By adding an EVestG depression feature from a previous study, separation accuracy of each PCS subgroup from controls was >90%. A four and three-group (excluding mild-depression subgroup) classification, achieved an accuracy of 74% and 81%, respectively. Correlation analysis indicated a significant correlation (R = 0.67) between the depression feature and the MADRS depression score as well as between the PCS-specific feature and Rivermead Post-Concussion Questionnaire (RPQ) (R = -0.48). No significant correlation was found between the PCS-specific feature and the MADRS score (R = 0.20) or between RPQ and the depression feature (R = 0.12). The (PCS-specific and depression-specific) EVestG features used herein have the potential to robustly detect and monitor changes, relatively independently, in both persistent PCS and its depression comorbidity. Clinically, this can be particularly advantageous.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Suleiman A,Lithgow B,Mansouri B,Moussavi Zdoi
10.1038/s41598-018-32808-1subject
Has Abstractpub_date
2018-09-27 00:00:00pages
14495issue
1issn
2045-2322pii
10.1038/s41598-018-32808-1journal_volume
8pub_type
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