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 language | English (US) |
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Article number | 9244589 |
Pages (from-to) | 1726-1736 |
Number of pages | 11 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 68 |
Issue number | 5 |
DOIs | |
State | Published - May 2021 |
Keywords
- Biomedical signal processing
- deconvolution
- optimization
- sparse recovery
- state-space methods
- system identification
- Skin Physiological Phenomena
- Galvanic Skin Response
- Sweating
- Humans
- Arousal
- Sympathetic Nervous System
ASJC Scopus subject areas
- Biomedical Engineering