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Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference

Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise i...

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Autores principales: Amin, Rafiul, Faghih, Rose T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9333288/
https://www.ncbi.nlm.nih.gov/pubmed/35900988
http://dx.doi.org/10.1371/journal.pcbi.1010275
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author Amin, Rafiul
Faghih, Rose T.
author_facet Amin, Rafiul
Faghih, Rose T.
author_sort Amin, Rafiul
collection PubMed
description Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis.
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spelling pubmed-93332882022-07-29 Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference Amin, Rafiul Faghih, Rose T. PLoS Comput Biol Research Article Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis. Public Library of Science 2022-07-28 /pmc/articles/PMC9333288/ /pubmed/35900988 http://dx.doi.org/10.1371/journal.pcbi.1010275 Text en © 2022 Amin, Faghih https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Amin, Rafiul
Faghih, Rose T.
Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference
title Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference
title_full Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference
title_fullStr Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference
title_full_unstemmed Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference
title_short Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference
title_sort physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9333288/
https://www.ncbi.nlm.nih.gov/pubmed/35900988
http://dx.doi.org/10.1371/journal.pcbi.1010275
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