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Exploring the use of Granger causality for the identification of chemical exposure based on physiological data
Wearable sensors offer new opportunities for the early detection and identification of toxic chemicals in situations where medical evaluation is not immediately possible. We previously found that continuously recorded physiology in guinea pigs can be used for early detection of exposure to an opioid...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053028/ https://www.ncbi.nlm.nih.gov/pubmed/37007435 http://dx.doi.org/10.3389/fnetp.2023.1106650 |
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author | Difrancesco, S. van Baardewijk, J. U. Cornelissen, A. S. Varon, C. Hendriks, R. C. Brouwer, A. M. |
author_facet | Difrancesco, S. van Baardewijk, J. U. Cornelissen, A. S. Varon, C. Hendriks, R. C. Brouwer, A. M. |
author_sort | Difrancesco, S. |
collection | PubMed |
description | Wearable sensors offer new opportunities for the early detection and identification of toxic chemicals in situations where medical evaluation is not immediately possible. We previously found that continuously recorded physiology in guinea pigs can be used for early detection of exposure to an opioid (fentanyl) or a nerve agent (VX), as well as for differentiating between the two. Here, we investigated how exposure to these different chemicals affects the interactions between ECG and respiration parameters as determined by Granger causality (GC). Features reflecting such interactions may provide additional information and improve models differentiating between chemical agents. Traditional respiration and ECG features, as well as GC features, were extracted from data of 120 guinea pigs exposed to VX (n = 61) or fentanyl (n = 59). Data were divided in a training set (n = 99) and a test set (n = 21). Minimum Redundancy Maximum Relevance (mRMR) and Support Vector Machine (SVM) algorithms were used to, respectively, perform feature selection and train a model to discriminate between the two chemicals. We found that ECG and respiration parameters are Granger-related under healthy conditions, and that exposure to fentanyl and VX affected these relationships in different ways. SVM models discriminated between chemicals with accuracy of 95% or higher on the test set. GC features did not improve the classification compared to traditional features. Respiration features (i.e., peak inspiratory and expiratory flow) were the most important to discriminate between different chemical’s exposure. Our results indicate that it may be feasible to discriminate between chemical exposure when using traditional physiological respiration features from wearable sensors. Future research will examine whether GC features can contribute to robust detection and differentiation between chemicals when considering other factors, such as generalizing results across species. |
format | Online Article Text |
id | pubmed-10053028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100530282023-03-30 Exploring the use of Granger causality for the identification of chemical exposure based on physiological data Difrancesco, S. van Baardewijk, J. U. Cornelissen, A. S. Varon, C. Hendriks, R. C. Brouwer, A. M. Front Netw Physiol Network Physiology Wearable sensors offer new opportunities for the early detection and identification of toxic chemicals in situations where medical evaluation is not immediately possible. We previously found that continuously recorded physiology in guinea pigs can be used for early detection of exposure to an opioid (fentanyl) or a nerve agent (VX), as well as for differentiating between the two. Here, we investigated how exposure to these different chemicals affects the interactions between ECG and respiration parameters as determined by Granger causality (GC). Features reflecting such interactions may provide additional information and improve models differentiating between chemical agents. Traditional respiration and ECG features, as well as GC features, were extracted from data of 120 guinea pigs exposed to VX (n = 61) or fentanyl (n = 59). Data were divided in a training set (n = 99) and a test set (n = 21). Minimum Redundancy Maximum Relevance (mRMR) and Support Vector Machine (SVM) algorithms were used to, respectively, perform feature selection and train a model to discriminate between the two chemicals. We found that ECG and respiration parameters are Granger-related under healthy conditions, and that exposure to fentanyl and VX affected these relationships in different ways. SVM models discriminated between chemicals with accuracy of 95% or higher on the test set. GC features did not improve the classification compared to traditional features. Respiration features (i.e., peak inspiratory and expiratory flow) were the most important to discriminate between different chemical’s exposure. Our results indicate that it may be feasible to discriminate between chemical exposure when using traditional physiological respiration features from wearable sensors. Future research will examine whether GC features can contribute to robust detection and differentiation between chemicals when considering other factors, such as generalizing results across species. Frontiers Media S.A. 2023-03-15 /pmc/articles/PMC10053028/ /pubmed/37007435 http://dx.doi.org/10.3389/fnetp.2023.1106650 Text en Copyright © 2023 Difrancesco, van Baardewijk, Cornelissen, Varon, Hendriks and Brouwer. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Network Physiology Difrancesco, S. van Baardewijk, J. U. Cornelissen, A. S. Varon, C. Hendriks, R. C. Brouwer, A. M. Exploring the use of Granger causality for the identification of chemical exposure based on physiological data |
title | Exploring the use of Granger causality for the identification of chemical exposure based on physiological data |
title_full | Exploring the use of Granger causality for the identification of chemical exposure based on physiological data |
title_fullStr | Exploring the use of Granger causality for the identification of chemical exposure based on physiological data |
title_full_unstemmed | Exploring the use of Granger causality for the identification of chemical exposure based on physiological data |
title_short | Exploring the use of Granger causality for the identification of chemical exposure based on physiological data |
title_sort | exploring the use of granger causality for the identification of chemical exposure based on physiological data |
topic | Network Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053028/ https://www.ncbi.nlm.nih.gov/pubmed/37007435 http://dx.doi.org/10.3389/fnetp.2023.1106650 |
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