Classifying Depression Severity in Recovery from Major Depressive Disorder via Dynamic Facial Features

Major depressive disorder is a common psychiatric illness. At present, there are no objective, non-verbal, automated markers that can reliably track treatment response. Here, we explore the use of video analysis of facial expressivity in a cohort of severely depressed patients before and after deep brain stimulation (DBS), an experimental treatment for depression. We introduced a set of variability measurements to obtain unsupervised features from muted video recordings, which were then leveraged to build predictive models to classify three levels of severity in the patients' recovery from depression. Multiscale entropy was utilized to estimate the variability in pixel intensity level at various time scales. A dynamic latent variable model was utilized to learn a low-dimensional representation of factors that describe the dynamic relationship between high-dimensional pixels in each video frame and over time. Finally, a novel elastic net ordinal regression model was trained to predict the severity of depression, as independently rated by standard rating scales. Our results suggest that unsupervised features extracted from these video recordings, when incorporated in an ordinal regression predictor, can discriminate different levels of depression severity during ongoing DBS treatment. Objective markers of patient response to treatment have the potential to standardize treatment protocols and enhance the design of future clinical trials.