A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning

Abstract

Pathological fear and anxiety disorders can have debilitating impacts on individual patients and society. The neural circuitry underlying fear learning and extinction has been known to play a crucial role in the development and maintenance of anxiety disorders. Pavlovian conditioning, where a subject learns an association between a biologically-relevant stimulus and a neutral cue, has been instrumental in guiding the development of therapies for treating anxiety disorders. To date, a number of physiological signal responses such as skin conductance, heart rate, electroencephalography and cerebral blood flow have been analyzed in Pavlovian fear conditioning experiments. However, physiological markers are often examined separately to gain insight into the neural processes underlying fear acquisition. We propose a method to track a single brain-related sympathetic arousal state from physiological signal features during fear conditioning. We develop a state-space formulation that probabilistically relates features from skin conductance and heart rate to the unobserved sympathetic arousal state. We use an expectation-maximization framework for state estimation and model parameter recovery. State estimation is performed via Bayesian filtering. We evaluate our model on simulated and experimental data acquired in a trace fear conditioning experiment. Results on simulated data show the ability of our proposed method to estimate an unobserved arousal state and recover model parameters. Results on experimental data are consistent with skin conductance measurements and provide good fits to heartbeats modeled as a binary point process. The ability to track arousal from skin conductance and heart rate within a state-space model is an important precursor to the development of wearable monitors that could aid in patient care. Anxiety and trauma-related disorders are often accompanied by a heightened sympathetic tone and the methods described herein could find clinical applications in remote monitoring for therapeutic purposes.

Closed-Loop Cognitive Stress Regulation Using Fuzzy Control in Wearable-Machine Interface Architectures

“…Compared to the existing approaches, which classify the stress levels based on the physiological data and provide different classes of stress levels, the proposed approach tracks the stress state in a more systematic way and in a continuous manner. The statespace model and Bayesian filter are in good agreement with the physiology underlying the sympathetic arousal activities [78], [79]. An increase in sympathetic arousal, which is a natural response to certain external stimuli, causes rise to measurable bio-signal changes such as skin conductance variations.…”

Examining the effect of stress on the flexible updating of avoidance responses

“…Yet their findings are based on SCRs, an outcome measure on a different response level of emotional arousal. Importantly, they are more closely linked to sympathetic nervous system activation and hence noradrenergic activity (Wickramasuriya & Faghih, 2020). SCRs, in contrast to US expectancy and avoidance responses, tap into different memory systems and do not always converge (e.g., Schultz et al., 2013).…”

Robust Inference of Autonomic Nervous System Activation Using Skin Conductance Measurements: A Multi-Channel Sparse System Identification Approach

“…Furthermore, we plan to include tonic component modelling for multi-channel approach to concurrently separate tonic and phasic components, similar to previous single channel approaches [19], [41]. To effectively and robustly estimate psychological stress level, we plan to utilize the inferred ANS activity from multi-channel SC recordings with other physiological signals similar to [42]- [49]. Finally, we intend to device appropriate control strategy to reduce the stress level estimated using the inferred ANS activity from a multichannel approach similar to [50]- [53].…”