Title:
A Skin-Like Sternal Patch to Monitor Autonomic Tone During Cognitive Stress and Sympathetic Arousals in Disordered Sleep
A Skin-Like Sternal Patch to Monitor Autonomic Tone During Cognitive Stress and Sympathetic Arousals in Disordered Sleep
Author(s)
Zavanelli, Nathan G.
Advisor(s)
Yeo, Woon-Hong
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Abstract
The central focus of this thesis is the development of skin-like wearable electronics and sensors that seamlessly integrate with the human body and provide hospital quality physiological monitoring and diagnostics in a simple, minimally obtrusive platform. One of the most poignant tragedies in modern medicine is that many pathologies with highly effective treatments remain undiagnosed, especially in marginalized communities. This suffering is fueled by a systemic failure in current diagnostics techniques: one the one hand, hospital grade in lab tests are expensive, low throughput, and ill-suited for continuous monitoring; on the other, wearable electronics are fundamentally limited by rigid mechanics and wired interfaces that prevent conformal skin contact, leading to poor signal quality and degraded long-term wearability. To address this critical shortcoming, I have conducted analytical, computational, empirical, and human subjects studies in soft materials and interfaces to enable a new class of wearable, wireless devices and sensors with mechanics finely tuned to transduce electrical, mechanical, and optical bio-signals from the human body. Whereas most medical research benefits only the most advantaged, my work is targeted specifically to the unique needs of marginalized communities, providing advanced diagnostic solutions to tackle some of the most pressing medical diagnostics challenges, both here in the United States and around the world.
Specifically, this thesis introduces a wireless, fully integrated, soft patch with skin-like mechanics optimized through analytical and computational studies to capture seismocardiograms, electrocardiograms, and photoplethysmograms from the sternum, allowing clinicians to investigate the cardiovascular response to acute changes in autonomic state. This system demonstrated exceptional promise in detecting sleep apneas, classifying sleep stage, identifying system vasoconstriction, and quantifying cognitive stress in trials with human subjects. This thesis focuses on the fundamental studies in soft materials, flexible electronics, signal processing and machine learning that enables these potentially transformative healthcare outcomes.
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Date Issued
2023-12-05
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Dissertation