Difrancesco S, van Baardewijk J U, Cornelissen A S, Varon C, Hendriks R C, Brouwer A M
Department Systems Biology, The Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands.
Department Human Performance, The Netherlands Organisation for Applied Scientific Research (TNO), Soesterberg, Netherlands.
Front Netw Physiol. 2023 Mar 15;3:1106650. doi: 10.3389/fnetp.2023.1106650. eCollection 2023.
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 ( = 61) or fentanyl ( = 59). Data were divided in a training set ( = 99) and a test set ( = 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.
在无法立即进行医学评估的情况下，可穿戴传感器为有毒化学物质的早期检测和识别提供了新机会。我们之前发现，豚鼠连续记录的生理数据可用于早期检测阿片类药物（芬太尼）或神经毒剂（VX）的暴露情况，以及区分这两种物质。在此，我们研究了暴露于这些不同化学物质如何影响由格兰杰因果关系（GC）确定的心电图（ECG）和呼吸参数之间的相互作用。反映这种相互作用的特征可能会提供额外信息，并改进区分化学制剂的模型。从120只暴露于VX（n = 61）或芬太尼（n = 59）的豚鼠数据中提取了传统呼吸和心电图特征以及GC特征。数据被分为训练集（n = 99）和测试集（n = 21）。分别使用最小冗余最大相关性（mRMR）和支持向量机（SVM）算法进行特征选择，并训练一个模型来区分这两种化学物质。我们发现，在健康状况下，心电图和呼吸参数存在格兰杰相关性，并且暴露于芬太尼和VX会以不同方式影响这些关系。SVM模型在测试集上区分化学物质的准确率达到95%或更高。与传统特征相比，GC特征并未改善分类效果。呼吸特征（即吸气和呼气峰值流量）对于区分不同化学物质暴露最为重要。我们的结果表明，使用可穿戴传感器的传统生理呼吸特征来区分化学物质暴露可能是可行的。未来的研究将考察在考虑其他因素（如跨物种推广结果）时，GC特征是否有助于对化学物质进行可靠的检测和区分。
