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Identifying ketamine responses in treatment-resistant depression using a wearable forehead EEG

Citation

Cao, Z and Lin, C-T and Ding, W and Chen, M-H and Li, C-T and Su, T-P, Identifying ketamine responses in treatment-resistant depression using a wearable forehead EEG, IEEE Transactions on Biomedical Engineering pp. 1-12. ISSN 0018-9294 (2018) [Refereed Article]

Copyright Statement

Copyright 2018 IEEE.

DOI: doi:10.1109/TBME.2018.2877651

Abstract

This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited 55 outpatients with TRD who were randomized into three approximately equal- sized groups (A: 0.5 mg/kg ketamine; B: 0.2 mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton Depression Rating Scale (HDRS) scores. At baseline, responders showed a significantly weaker EEG theta power than did non- responders (p<0.05). Responders exhibited a higher EEG alpha power but lower EEG alpha asymmetry and theta cordance at post-treatment than at baseline (p<0.05). Furthermore, our baseline EEG predictor classified responders and non-responders with 81.3 9.5% accuracy, 82.1 8.6% sensitivity and 91.9 7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry and cordance at baseline and early post-treatment changes. The prefrontal EEG patterns at baseline may account for recognizing ketamine effects in advance. Our randomized, double- blind, placebo-controlled study provides information regarding clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.

Item Details

Item Type:Refereed Article
Keywords:EEG, depression, forehead, ketamine, predictor
Research Division:Information and Computing Sciences
Research Group:Computation Theory and Mathematics
Research Field:Numerical Computation
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
ID Code:131536
Year Published:2018
Web of Science® Times Cited:3
Deposited By:Information and Communication Technology
Deposited On:2019-03-21
Last Modified:2019-05-13
Downloads:0

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