Abstract

Medical diagnostic devices are in high demand due to increasing cases of neurodegenerative diseases in the aging population and pandemic outbreaks in our increasingly connected global community. Devices capable of detecting the presence of a disease in its early stages can have dramatic impacts on how it can be treated or eliminated. High cost and limited accessibility to diagnostic tools are the main barriers preventing potential patients from receiving a timely disease diagnosis. This dissertation presents several devices that are aimed at providing higher quality medical diagnostics at a low cost. Brain function is commonly studied with systems detecting the action potentials that are formed when neurons fire. CMOS technology enables extremely high-density electrode arrays to be produced with integrated amplifiers for high-throughput action potential measurement systems while greatly reducing the cost per measurement compared to traditional tools. Recently, CMOS technology has also been used to develop high-throughput electrochemical measurement systems. While action potentials are important, communication between neurons occurs by the flow of neurotransmitters at the synapses, so measurement of action potentials alone is incapable of fully studying neurotransmission. In many neurodegenerative diseases the breakdown in neurotransmission begins well before the disease manifests itself. The development of a dual-mode CMOS device that is capable of simultaneous high-throughput measurement of both action potentials and neurotransmitter flow via an on-chip electrode array is presented in this dissertation. This dual-mode technology is useful to those studying the dynamic decay of the neurotransmission process seen in many neurodegenerative diseases using a low-cost CMOS chip. This dissertation also discusses the development of more traditional diagnostic devices relying on PCR, a method commonly used only in centralized laboratories and not readily available at the point-of-care. These technologies will enable faster, cheaper, more accurate, and more accessible diagnostics to be performed closer to the patient.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2022

Semester

Summer

Advisor

Kim, Brian

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0009657; DP0027588

URL

https://purls.library.ucf.edu/go/DP0027588

Language

English

Release Date

February 2023

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Download

Included in

Biomedical Commons

Share

COinS