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The extent to which advanced waveform analysis of non-invasive physiologicalsignals can diagnose levels of hypovolemia remains insufficiently explored. Thepresent study explores the discriminative ability of a deep learning (DL)framework to classify levels of ongoing hypovolemia, simulated via noveldynamic lower body negative pressure (LBNP) model among healthy volunteers. Weused a dynamic LBNP protocol as opposed to the traditional model, where LBNP isapplied in a predictable step-wise, progressively descending manner. Thisdynamic LBNP version assists in circumventing the problem posed in terms oftime dependency, as in real-life pre-hospital settings, intravascular bloodvolume may fluctuate due to volume resuscitation. A supervised DL-basedframework for ternary classification was realized by segmenting the underlyingnoninvasive signal and labeling segments with corresponding LBNP target levels.The proposed DL model with two inputs was trained with respectivetime-frequency representations extracted on waveform segments to classify eachof 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 modelvia late fusion among both inputs assists in enhanced classificationperformance. When evaluated in a 3-fold cross-validation setup with stratifiedsubjects, the experimental findings demonstrated PPG to be a potentialsurrogate for variations in blood volume with average classificationperformance, AUROC: 0.8861, AUPRC: 0.8141, $F1$-score:72.16%, Sensitivity:79.06%, and Specificity:89.21 %. Our proposed DL algorithm on PPG signaldemonstrates the possibility of capturing the complex interplay inphysiological responses related to both bleeding and fluid resuscitation usingthis challenging LBNP setup.

Non-invasive Waveform Analysis for Emergency Triage via Simulated  Hemorrhage: An Experimental Study using Novel Dynamic Lower Body Negative  Pressure Model