  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 of capturing the complex interplay in
physiological responses related to both bleeding and fluid resuscitation using
this challenging LBNP setup.
