Towards using Breathing Features for Multimodal Estimation of Depression Severity

Breathing patterns are shown to have strong correlations with emotional states, and hence have promise for automatic mood order prediction and analysis. An essential challenge here is the lack of ground truth for breathing sounds, especially for medical and archival datasets. In this study, we provide a cross-dataset approach for breathing pattern prediction and analyse the contribution of predicted breath signals for the detection of depressive states, using the DAIC-WOZ corpus. We use interpretable features in our models to provide actionable insights. Our experimental evaluation shows that in participants with higher depression scores (as indicated by the eight-item Patient Health Questionnaire, PHQ-8), breathing events tend to be shallow or slow. We furthermore tested linear and non-linear regression models with breathing, linguistic sentiment and conversational features, and show that these simple models outperform the AVEC17 Real-life Depression Recognition Sub-challenge baseline.