Discriminating Clinical Phases of Recovery From Major Depressive Disorder using the Dynamics of Facial Expression
Halls department, Hall 6
Date and Time
Wednesday, 27 December 2017
17:00 - 18:00
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.
Sahar Harati is a PhD student in the Department of Biomedical Informatics at the Emory University School of Medicine. She got her B.S. degree in Computer Engineering from Sharif University of Technology in 2013. Her current research interests include machine learning, computational psychiatry, and video processing. Currently, she is working on modeling Depression and Recovery using Audio-Visual Features