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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9333288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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