Exploring the use of Granger causality for the identification of chemical exposure based on physiological data.

Autor:	Difrancesco S; Department Systems Biology, The Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands., van Baardewijk JU; Department Human Performance, The Netherlands Organisation for Applied Scientific Research (TNO), Soesterberg, Netherlands., Cornelissen AS; Department CBRN Protection, The Netherlands Organisation for Applied Scientific Research (TNO), Rijswijk, Netherlands., Varon C; Circuits and Systems (CAS) Group, Delft University of Technology, Delft, Netherlands.; Centre for Research and Engineering in Space Technologies-CREST, Université Libre de Bruxelles, Brussels, Belgium., Hendriks RC; Circuits and Systems (CAS) Group, Delft University of Technology, Delft, Netherlands., Brouwer AM; Department Human Performance, The Netherlands Organisation for Applied Scientific Research (TNO), Soesterberg, Netherlands.
Jazyk:	angličtina
Zdroj:	Frontiers in network physiology [Front Netw Physiol] 2023 Mar 15; Vol. 3, pp. 1106650. Date of Electronic Publication: 2023 Mar 15 (Print Publication: 2023).
DOI:	10.3389/fnetp.2023.1106650
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Difrancesco, van Baardewijk, Cornelissen, Varon, Hendriks and Brouwer.)
Databáze:	MEDLINE
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