Identification of Sympathetic Nervous System Activation from Skin Conductance: A Sparse Decomposition Approach with Physiological Priors

Md Rafiul Amin, Rose T. Faghih

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Objective: Sweat secretions lead to variations in skin conductance (SC) signal. The relatively fast variation of SC, called the phasic component, reflects sympathetic nervous system activity. The slow variation related to thermoregulation and general arousal is known as the tonic component. It is challenging to decompose the SC signal into its constituents to decipher the encoded neural information related to emotional arousal. Methods: We model the phasic component using a second-order differential equation representing the diffusion and evaporation processes of sweating. We include a sparse impulsive neural signal that stimulates the sweat glands for sweat production. We model the tonic component with several cubic B-spline functions. We formulate an optimization problem with physiological priors on system parameters, a sparsity prior on the neural stimuli, and a smoothness prior on the tonic component. Finally, we employ a generalized-cross-validation-based coordinate descent approach to balance among the smoothness of the tonic component, the sparsity of the neural stimuli, and the residual. Results: We illustrate that we can successfully recover the unknowns separating both tonic and phasic components from both experimental and simulated data (with R2 > 0.95). Further, we successfully demonstrate our ability to automatically identify the sparsity level for the neural stimuli and the smoothness level for the tonic component. Conclusion: Our generalized-cross-validation-based novel method for SC signal decomposition successfully addresses previous challenges and retrieves a physiologically plausible solution. Significance: Accurate decomposition of SC could potentially improve cognitive stress tracking in patients with mental disorders.

    Original languageEnglish (US)
    Article number9244589
    Pages (from-to)1726-1736
    Number of pages11
    JournalIEEE Transactions on Biomedical Engineering
    Volume68
    Issue number5
    DOIs
    StatePublished - May 2021

    Keywords

    • Biomedical signal processing
    • deconvolution
    • optimization
    • sparse recovery
    • state-space methods
    • system identification

    ASJC Scopus subject areas

    • Biomedical Engineering

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