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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, 66, (6) pp. 1668-1679. ISSN 0018-9294 (2019) [Refereed Article]

Copyright Statement

Copyright 2018 IEEE

DOI: doi:10.1109/TBME.2018.2877651

Abstract

This study explores responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited and randomly assigned 55 outpatients with TRD 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 scores. At baseline, the responders showed significantly weaker EEG theta power than the non-responders (p<0.05). Compared to the baseline, the responders exhibited higher EEG alpha power but lower EEG alpha asymmetry and theta cordance post-treatment (p<0.05). Furthermore, our baseline EEG predictor classified the 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. Prefrontal EEG patterns at baseline may serve as indicators of ketamine effects. Our randomized double-blind placebo-controlled study provides information regarding the 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, entropy, depression, forehead, ketamine, predictor
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:132866
Year Published:2019
Web of Science® Times Cited:4
Deposited By:Information and Communication Technology
Deposited On:2019-05-23
Last Modified:2019-08-22
Downloads:0

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