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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression
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Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression

机译：使用面部表情动力学区分重度抑郁症的临床恢复阶段

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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.
机译：我们使用了多种可变性指标，从深部脑刺激（DBS）治疗重大抑郁症（MDD）之前和之后的患者录像中提取了无监督的特征。我们的目标是量化对面部表情的治疗效果。多尺度熵（MSE）用于捕获多个时间尺度上像素强度级别的时间变化。动态潜变量模型（DLVM）用于学习低维（D = 20）的动态因子集，这些因子解释了每个视频帧内和跨时间在高维像素（M = 30×30）上观察到的协方差。我们的初步结果表明，从这些录像中获得的无监督功能可以区分抑郁症和康复的不同阶段。这项研究的总体目标是开发更精细的临床抑郁症治疗反应标记物。

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