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Title: Continuous Estimation of Blood Volume Status using Wearable Sensing and Machine Learning
Committee:
Dr. Inan, Advisor
Dr. Y. Zhang, Chair
Dr. Kamaleswaran
Abstract: The objective of the proposed research is to develop a system comprised of wearable sensing and machine learning algorithms to continuously estimate an individual’s hypovolemic or blood volume status. Hypovolemia is a leading cause of preventable death, with many potentially overlapping causes occurring in both hospital and field locations. The objective of this work is to explore whether a multi-modal wearable system comprised of electro-mechanical cardiac sensors paired with machine learning algorithms is sufficiently capable of estimating an individual’s blood volume status noninvasively. A key goal of such a system would be to estimate where the individual is on the path to cardiac decompensation. To this end, an intensive large animal study was carried out in which noninvasive signals and catheter pressure waveforms were recorded as the animals underwent changes in relative and absolute blood volume. Features were extracted from both the noninvasive and catheter signals and compared against each other for quality. These features were used to train a model to predict individual-specific hypovolemic status during hemorrhage. This model was later expanded to include training data from the entire protocol so as to be able to predict hypovolemic status during relative and absolute hypovolemia as well as during resuscitation. As part of the proposed work, a new model will be developed to predict decompensation status in hospital patients with sepsis, building on what was learned from the preclinical work. These models would show that continuous noninvasive estimation of decompensation status is possible during absolute and relative hypovolemia from the same multi-modal wearable device.
