Non-invasive waveform analysis for emergency triage via simulated hemorrhage: An experimental study using novel dynamic lower body negative pressure model

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2023-07-01 DOI:10.1016/j.bbe.2023.06.002
Naimahmed Nesaragi , Lars Øivind Høiseth , Hemin Ali Qadir , Leiv Arne Rosseland , Per Steinar Halvorsen , Ilangko Balasingham
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Abstract

The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed in terms of time dependency, as in real-life pre-hospital settings intravascular blood volume may fluctuate due to volume resuscitation. A supervised DL-based framework for ternary classification was realized by segmenting the underlying noninvasive signal and labeling segments with corresponding LBNP target levels. The proposed DL model with two inputs was trained with respective time–frequency representations extracted on waveform segments to classify each of them into blood volume loss: Class 1 (mild); Class 2 (moderate); or Class 3 (severe). At the outset, the latent space derived at the end of the DL model via late fusion among both inputs assists in enhanced classification performance. When evaluated in a 3-fold cross-validation setup with stratified subjects, the experimental findings demonstrated PPG to be a potential surrogate for variations in blood volume with average classification performance, AUROC: 0.8861, AUPRC: 0.8141, F1-score:72.16%, Sensitivity:79.06%, and Specificity:89.21%. Our proposed DL algorithm on PPG signal demonstrates the possibility to capture the complex interplay in physiological responses related to both bleeding and fluid resuscitation using this challenging LBNP setup.

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模拟出血急诊分诊的无创波形分析:基于新型动态下体负压模型的实验研究
非侵入性生理信号的高级波形分析可以在多大程度上诊断低血容量水平,目前还没有得到充分的探索。本研究探讨了深度学习(DL)框架在健康志愿者中通过新型动态下半身负压(LBNP)模型模拟的持续低血容量水平分类的判别能力。与传统模型相反,我们使用了动态LBNP协议,在传统模型中,LBNP以可预测的逐步递减方式应用。这种动态LBNP版本有助于避免时间依赖性方面的问题,因为在现实生活中的院前环境中,血管内血容量可能会因容量复苏而波动。通过分割潜在的无创信号并用相应的LBNP靶水平标记片段,实现了基于监督DL的三元分类框架。所提出的具有两个输入的DL模型使用在波形段上提取的各自的时间-频率表示进行训练,以将每个波形段分类为血容量损失:1类(轻度);2级(中等);或3级(严重)。一开始,通过两个输入之间的后期融合在DL模型末尾导出的潜在空间有助于增强分类性能。当在分层受试者的3倍交叉验证设置中进行评估时,实验结果表明PPG是血容量变化的潜在替代品,具有平均分类性能,AUROC:0.861,AUPRC:0.8141,F1得分:72.16%,灵敏度:79.06%,特异性:89.21%。我们提出的PPG信号DL算法证明了使用这种具有挑战性的LBNP设置捕捉与出血和液体复苏相关的生理反应中复杂相互作用的可能性。
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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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