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

Md Rafiul Amin,Rose T Faghih

doi:10.1109/access.2019.2956673

Md Rafiul Amin, Rose T Faghih

Open Access

https://doi.org/10.1109/access.2019.2956673

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Abstract

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.

Highlights

Electrodermal activity (EDA) refers to any alterations in the electrical characteristics of the skin caused by salty sweat secretion
We describe the system dynamics using the following set of differential equations each denoting the kinetics of sweat secretion and evaporation process in sweat glands [16], [24], [37], τr τd d 2 ζ1 (t ) dt 2
We use the proposed algorithm and concurrently deconvolve skin conductance (SC) measurements from the middle phalanx of hand and the medial plantar surface of foot collected during an auditory stimulation experiment and recover the underlying stimuli u(t), the corresponding rise time and decay times of SC responses, and the attenuation (α2) at the medial plantar surface of foot with respect to the middle phalanx of hand

Summary

Introduction

Electrodermal activity (EDA) refers to any alterations in the electrical characteristics of the skin caused by salty sweat secretion. Hypothalamic control of sweating is primarily intended for thermoregulation of the human body. Apart from thermoregulation, sweating can take place due to other physiological events including emotional arousal [1], [2]. Alterations in SC throughout the different skin regions are regulated by the autonomic nervous system (ANS) [5]. The analysis of SC time series assumed to be modulated by the ANS can potentially be used to track the mental health of an individual in order to prevent mental stress-related problems [6]

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