Graduation Year

2023

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Robert Karam, Ph.D.

Committee Member

Mehran Mozaffari Kermani, Ph.D.

Committee Member

Srinivas Katkoori, Ph.D.

Committee Member

Nasir Ghani, Ph.D.

Committee Member

Kaiqi Xiong, Ph.D.

Keywords

Embedded Security, Implantable Sensors, Urodynamic Studies

Abstract

Lower urinary tract dysfunction (LUTD) is a debilitating medical condition that affects millions of individuals worldwide. Urodynamics is the current gold standard for diagnosing LUTD but uses non-physiologically fast, retrograde cystometric filling to obtain a brief snapshot of bladder function. Current state-of-the-art research in bladder monitoring includes ambulatory urodynamics using wireless implantable devices to evaluate bladder function during natural filling for long-term monitoring. However, there are various challenges and limitations to this multi-sensor approach. This research focuses on developing frameworks for automated event detection, data analysis, and optimization of long-term bladder recordingsto improve the diagnosis and treatment of LUTD. In particular, this work proposes the estimation of bladder detrusor signal from single-channel recordings using signal processing and neural network techniques, integration of accelerometry and bladder volume signals into the event detection framework using sensor fusion techniques, and optimization of sensing and event detection parameters using machine learning for system power reduction and reliability enhancement. It also explores the security concerns in wireless medical technology and presents a proof of concept hardware immune system based anti-malware solution that is suitable for low-power, resource-constrained, and network-facing embedded Internet of Things (IoT) or Internet of Medical Things (IoMT) devices.

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