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

The autonomic nervous system (ANS) stimulates various sweat glands for maintaining body temperature as well as in response to various psychological events. Variations in skin conductance (SC) measurements due to salty sweat secretion can be used to infer the underlying ANS activity. Recovering both ANS activity and the underlying system from noisy single-channel recordings is challenging. As the same ANS activity drives all the sweat glands throughout the skin, the same information is encoded in different SC recordings. We perform system identification and develop a physiological model for multi-channel SC recordings relating them to ANS activation events. Using a multi-rate formulation, we estimate the number, timings, and amplitudes of ANS activity and the unknown model parameters from multi-channel SC data. We incorporate a generalized-cross-validation-based sparse recovery approach to balance between the sparsity level of the inferred ANS activity and the goodness of fit to the multi-channel SC data. We successfully deconvolve multi-channel experimental auditory stimulation SC data from human participants. We analyze experimental and simulated data to validate the performance of our concurrent deconvolution algorithm; we illustrate that we can recover the ANS activity due to the underlying auditory stimuli. Furthermore, we estimate stress using inferred ANS activity based on multi-channel deconvolution of SC data collected during different driving conditions and at rest. We propose a model for multi-channel SC recordings. Moreover, we develop a multi-channel deconvolution approach to perform robust sparse inference in the presence of noise. The proposed approach could potentially improve stress state estimation using wearables.