Title:
A NOVEL MULTI-MODAL, WEARABLE SENSING SYSTEM TO AUTOMATICALLY QUANTIFY CHANGES IN EXTRAVASCULAR FLUID LEVELS
A NOVEL MULTI-MODAL, WEARABLE SENSING SYSTEM TO AUTOMATICALLY QUANTIFY CHANGES IN EXTRAVASCULAR FLUID LEVELS
Author(s)
Mabrouk, Samer
Advisor(s)
Inan, Omer T.
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Abstract
The buildup of static edematous fluids (swelling) in the tissue is indicative of a serious medical condition that can lead to long-term tissue damage, reduction in mobility and in some cases loss of limb. This swelling can be due to internal factors such as an immunoresponse to injuries or infections, or external factors such as a leakage of infused intravenous medication to the surrounding tissue (i.e., IV infiltration or extravasation). Detecting and tracking changes in a tissue’s extracellular fluid content is crucial in diagnosing the severity of the injury and/or infection, and thereby preventing irreversible tissue damage. However, current methods for quantifying fluid levels in the extravascular space are either (1) manual and subjective, relying heavily on the medical staff’s expertise, or (2) costly and timely, such as X-rays or magnetic resonance imaging (MRI).
In this dissertation, I present non-invasive wearable technologies that utilize localized bioimpedance contextualized by the tissue’s kinematics to robustly quantify changes in the biological tissue’s extracellular fluid content. Towards this goal, several robust and miniaturized systems are designed and implemented by researching different integrated circuits, analog front ends, and novel physiology-driven calibration techniques that together increase the system’s accuracy and reduce its size and power consumption. Next, novel methods and algorithms are developed to allow for unobtrusive real-time detection of changes in extracellular fluid content. The systems, methods and algorithms were validated in human subjects studies, animal models and cadaver models for ankle edema tracking, and in human subjects studies and animal tissue for intravenous infiltration detection.
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Date Issued
2020-12-01
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Text
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Dissertation