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dc.contributor.advisorInan, Omer T
dc.contributor.authorZia, Jonathan
dc.date.accessioned2021-09-15T15:32:22Z
dc.date.available2021-09-15T15:32:22Z
dc.date.created2020-08
dc.date.issued2020-06-11
dc.date.submittedAugust 2020
dc.identifier.urihttp://hdl.handle.net/1853/64971
dc.description.abstractThe objective of this research is to provide a mathematical and conceptual foundation for the processing and analysis of cardiomechanical signals. We begin by exploring a potential clinical application of this technology, using a multi-modal wearable system to accurately track the progression toward hypovolemic shock in an animal model of hemorrhage. In this manner, we demonstrate the potential for cardiomechanical sensing to enable data-driven triage and management of trauma injury. Capturing these signals from wearable systems, however, is a difficult task, creating a barrier to widespread application. To enable more robust analysis of these signals, we begin by presenting a unified method of determining signal quality and localizing the position of the cardiomechanical sensors on the chest wall by analyzing population-level patterns in signal morphology. Next, we develop and explore the idea that observed cardiomechanical signals – while noisy and complex in the time domain – derive from a simple low-dimensional dynamic process. By understanding and modeling these dynamics, we may perform more robust extraction of physiological data from these signals, as well as enabling higher-level tasks such as algorithmic compensation for sensor misplacement.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectsignal informatics
dc.subjectbioinformatics
dc.subjectcardiomechanical sensing
dc.subjectseismocardiography
dc.subjectmachine learning
dc.titleOn the assessment of cardiomechanical function via wearable sensing systems: harnessing population-level patterns and dynamics for robust physiological monitoring
dc.typeText
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberRozell, Christopher
dc.contributor.committeeMemberDavenport, Mark
dc.contributor.committeeMemberHahn, Jin-Oh
dc.contributor.committeeMemberEtemadi, Mozziyar
dc.type.genreDissertation
dc.date.updated2021-09-15T15:32:23Z


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