Sparse Deconvolution of Electrodermal Activity via Continuous-Time System Identification.

Abstract

Electrodermal activity (EDA) indicates different eccrine sweat gland activity caused by the stimulation of the autonomic nervous system. Recovering the number, timings, and amplitudes of underlying neural stimuli and physiological system parameters from the EDA is a challenging problem. One of the challenges with the existing methods is the non-convexity of the optimization formulations for estimating the parameters given the stimuli. We solve this parameter estimation problem using the following continuous-time system identification framework: 1) we specifically use the Hartley modulating function (HMF) for parameter estimation so that the optimization formulation for estimating the parameters given the stimuli is convex; and 2) we use Kaiser windows with different shape parameters to put more emphasis on the significant spectral components so that there is a balance between filtering out the noise and capturing the data. We apply this algorithm to skin conductance (SC) data, a measure of EDA, collected during cognitive stress experiments. Under a sparsity constraint, in the HMF domain, we successfully deconvolve the SC signal. We obtain number, timings, and amplitudes of the underlying neural stimuli along with the system parameters with R2 above 0.915. Moreover, using simulated data, we illustrate that our approach outperforms the existing EDA data analysis methods, in recovering underlying stimuli. We develop a novel approach for deconvolution of SC by employing the HMF method and capturing the significant spectral components of SC data. Recovering the underlying neural stimuli more accurately using this approach will potentially improve tracking emotional states in affective computing.