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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Processing and Analysis of the Seismocardiogram to Enable Estimations of Blood Volume Decompensation Status
Committee:
Dr. Omer Inan, ECE, Chair, Advisor
Dr. Ying Zhang, ECE
Dr. Rishikesan Kamaleswaran, Emory
Dr. Michael Sawka, BioSciences
Dr. Jin-Oh Hahn, Univ of Maryland
Abstract:
Title: Processing and Analysis of the Seismocardiogram to Enable Estimations of Blood Volume Decompensation Status
Committee:
Dr. Omer Inan, ECE, Chair, Advisor
Dr. Ying Zhang, ECE
Dr. Rishikesan Kamaleswaran, Emory
Dr. Michael Sawka, BioSciences
Dr. Jin-Oh Hahn, Univ of Maryland
Abstract: The objective of this research was 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 noninvasive electro-mechanical cardiac sensors paired with machine learning is sufficiently capable of estimating an individual's blood volume status. 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 to predict hypovolemic status during relative and absolute hypovolemia as well as during resuscitation. The features derived from the seismocardiogram were determined to be key in estimating hypovolemic status. The processing of this signal is still in the early stages, subject to many types of noise and requiring human oversight. The remainder of this work presents three algorithms developed for reliably processing seismocardiogram signals and indicates future directions for research.
