A Multimodal Dataset and Evaluation for Feature Estimators of Temporal Phases of Anxiety

Abstract

Vicious cycles of anxiety responses underlie the onset of increasingly prevalent and highly impairing anxiety disorders and also contribute to their maintenance. Our goal is to evaluate whether different anxiety responses are evident in temporal patterns of physiological and behavioral features. Consequently, we established a rich multimodal-multisensor dataset of cardiac, electrodermal, movement, posture, and speech measures from 95 young adults during two anxiety experiments that induce social anxiety and bug-phobic anxiety. A subset of this dataset is publicly available at "Anxiety Phases Dataset"Figshare repository. We adopted a generalized mixed model approach and found that 10 out of 14 feature trajectories modeled for high- and low-anxiety groups differ significantly at 0.001 level in magnitude, creating at least two temporal phases in both groups. Further differences in magnitude, duration and the number of phases were observed for responses of confrontation, safety behaviors, escape, and avoidance in the high-anxiety group. Our findings contribute to the long-term aim of designing multimodal systems that have great potential to reduce the impacts of anxiety disorders and improve therapy.