Print Email Facebook Twitter 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 Author Difrancesco, S. (TNO) van Baardewijk, J.U. (TNO) Cornelissen, A.S. (TNO) Varon, Carolina (TU Delft Signal Processing Systems; Universite' Libre de Bruxelles (ULB)) Hendriks, R.C. (TU Delft Signal Processing Systems) Bouwer, A.M. (TNO) Date 2023 Abstract 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 Subject Granger causalitychemical exposuretoxidrome detectionphysiological datasupport vector machinemachine learning To reference this document use: http://resolver.tudelft.nl/uuid:e67b52d7-89d1-45f4-998f-e8c53fd25871 DOI https://doi.org/10.3389/fnetp.2023.1106650 Source Frontiers in Network Physiology, 3 Part of collection Institutional Repository Document type journal article Rights © 2023 S. Difrancesco, J.U. van Baardewijk, A.S. Cornelissen, Carolina Varon, R.C. Hendriks, A.M. Bouwer Files PDF fnetp_03_1106650.pdf 2.06 MB Close viewer /islandora/object/uuid:e67b52d7-89d1-45f4-998f-e8c53fd25871/datastream/OBJ/view