Open Access Repository

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

Cao, Z ORCID: 0000-0003-3656-0328, Lin, C-T, Ding, W, Chen, M-H, Li, C-T and Su, T-P 2019 , 'Identifying ketamine responses in treatment-resistant depression using a wearable forehead EEG' , IEEE Transactions on Biomedical Engineering, vol. 66, no. 6 , pp. 1668-1679 , doi: 10.1109/TBME.2018.2877651.

Full text not available from this repository.

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 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 Type: Article
Authors/Creators:Cao, Z and Lin, C-T and Ding, W and Chen, M-H and Li, C-T and Su, T-P
Keywords: EEG, entropy, depression, forehead, ketamine, predictor
Journal or Publication Title: IEEE Transactions on Biomedical Engineering
Publisher: Ieee-Inst Electrical Electronics Engineers Inc
ISSN: 0018-9294
DOI / ID Number: 10.1109/TBME.2018.2877651
Copyright Information:

Copyright 2018 IEEE

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP