Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression.
Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression.
复制标题
利用面部表情的动态来区分重度抑郁症恢复的临床阶段。
DOI：
10.1109/embc.2016.7591178
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Nemati,Shamim
中科院分区：
文献类型：
--
作者：
Harati,Sahar;Crowell,Andrea;Mayberg,Helen;JunKong;Nemati,Shamim
我们使用了几个变异性指标，从深度脑刺激（DBS）治疗重度抑郁症（MDD）前后的患者视频记录中提取无监督特征。我们的目标是量化治疗对面部表情的影响。多尺度熵（MSE）被用来捕捉在多个时间尺度上的像素强度水平的时间变化。动态潜变量模型（DLVM）用于学习一组低维（D = 20）动态因子，这些因子解释了每个视频帧内和时间上观察到的高维像素（M = 30 × 30）之间的协方差。我们的初步结果表明，从这些视频记录中学习的无监督特征可以区分抑郁和恢复的不同阶段。这项研究的总体目标是开发更精确的抑郁症治疗临床反应的标志物。
We used several metrics of variability to extract unsupervised features from video recordings of patients before and after deep brain stimulation (DBS) treatment for major depressive disorder (MDD). Our goal was to quantify the treatment effects on facial expressivity. Multiscale entropy (MSE) was used to capture the temporal variability in pixel intensity level at multiple time-scales. A dynamic latent variable model (DLVM) was used to learn a low dimensional (D = 20) set of dynamic factors that explain the observed covariance across the high-dimensional pixels (M = 30 × 30) within each video frame and across time. Our preliminary results indicate that unsupervised features learned from these video recordings can distinguish different phases of depression and recovery. The overarching goal of this research is to develop more refined markers of clinical response to treatment for depression.