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摘要翻译：
本研究使用可穿戴式前额脑电图(EEG)装置探讨氯胺酮对治疗抵抗性抑郁症(TRD)患者的反应。我们招募55名门诊TRD患者，在双盲条件下随机分为三个大小大致相等的组(A:0.5mg/kg氯胺酮、B:0.2mg/kg氯胺酮和C：生理盐水）。用脑电信号和汉密尔顿抑郁量表(HDRS)评分测定氯胺酮反应。在基线时，应答者的脑电图θ功率明显弱于非应答者(p<0.05)。治疗后应答者的脑电α功率高于治疗前，但α不对称性和θ相关性低于治疗前(p<0.05)。此外，我们的基线脑电图预测器以81.3±-9.5%的准确性、82.1±-8.6%的敏感性和91.9±-7.4%的特异性对应答者和非应答者进行了分类。总之，混合剂量氯胺酮的快速抗抑郁效果与前额叶脑电功率、基线时的不对称性和相关性以及治疗后早期的变化有关。基线时的前额叶脑电图模式可能解释了提前识别氯胺酮效应。我们的随机、双盲、安慰剂对照研究提供了关于对潜在靶点的临床影响的信息，这些靶点是TRD患者基础识别和氯胺酮效应的早期变化的基础。
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英文标题：
《Identifying Ketamine Responses in Treatment-Resistant Depression Using a
Wearable Forehead EEG》
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作者：
Zehong Cao, Chin-Teng Lin, Weiping Ding, Mu-Hong Chen, Cheng-Ta Li,
Tung-Ping Su
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited fifty-five outpatients with TRD who were randomised 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 recognising ketamine effects in advance. Our randomised, 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.
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PDF链接：
https://arxiv.org/pdf/1805.11446
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