StressFADS: Learning Latent Autonomic Factors of Stress in the Context of Trauma Recall and Neuromodulation
Physiological markers of stress and neuromodulation (e.g., heart rate variability) are often inconsistent when it comes to quantifying changes in autonomic nervous system function. This inconsistency is explained by the autonomic nervous system's output varying across organ systems, as well as limitations in what each marker quantifies. In this work, we present an unsupervised learning approach we term StressFADS: Stress Factor Analysis via Dynamical Systems. StressFADS overcomes single marker inconsistencies by learning underlying dynamics that are shared across physiological markers of stress. StressFADS's encoder summarizes a time window of physiological markers and initializes a recurrent neural network (RNN) with this summary. This RNN is autonomously simulated forward in time, and the output at each timestep is fed through a dimension-ality reduction stage trained to reconstruct the original window of physiological markers. This forces the model to learn latent representations that capture shared dynamics across the markers. We apply StressFADS to the analysis of approximately 50 hours of 1-Hz cardiovascular and respiratory marker time series from a double-blind, randomized controlled trial (N = 26) involving trauma recall and active or sham cervical transcutaneous vagus nerve stimulation (tVNS). We find that StressFADS learned latent factors that successfully quantify differences between stress induced by trauma recall, a neutral condition, and active or sham tVNS. This is promising and motivates future work in learning latent autonomic states that more faithfully track changes in stress and intervention effects for just-in-time stress mitigation.
