Luan, Bo
(2018)
Single-Unit Leadless EEG Sensor.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Non-convulsive seizure (NCS) and non-convulsive status epilepticus (NCSE) are severe neurological disorders within intensive care units (ICUs) and emergency departments (EDs). Traditionally, physiological monitoring in ICUs and EDs focuses on cardiopulmonary variables, including blood pressure and heart rate. The neurological conditions, on the other hand, are often assessed by bedside observations from physicians. Without proper monitoring tools, the NCS and NCSE that lack observable clinical manifestations are easily overlooked or misdiagnosed. The problem can be amplified among patients with impaired consciousness who cannot respond to environmental stimuli. The delayed detection and treatment lead to substantial morbidity, mortality, and healthcare costs.
Currently, electroencephalography (EEG) is the most effective diagnostic tool for NCS and NCSE in ICUs and EDs. Meanwhile, less than two percent of the critically ill patients in ICUs and EDs are undergoing EEG. The under-adoption or decreased utilization of EEG originates from challenges to accommodate EEG into established practice protocols. Therefore, the timely acquisition of EEG has been one of the paramount needs in today’s emergency care.
This dissertation presents a novel EEG sensor that is leadless, self-contained, and the size of a U.S. Penny. The sensor enables rapid EEG setup and efficient EEG acquisition. The dissertation first investigated into a novel EEG electrode-structure enclosing four unique arc-shaped electrodes. We demonstrated the feasibility of such electrode configuration by experimental investigations on both a physical model and a healthy human subject. The dissertation then presented Monte Carlo simulations to predict the statistical performance of the single-unit sensor on the whole brain. A forward computation algorithm was implemented to compute the scalp potential in response to dipolar sources within an analytically modeled brain. The data-informed findings indicated that the whole-brain quantitative performance of this electrode configuration is comparable to the cup electrode currently used as the gold standard. The results are presented in a multi-variant probability density function. Taken a step further, a deterministic solution to such probability model was derived. These results provide insights into the workings of the single-unit sensor. Furthermore, a single-unit sensor prototype was constructed with a specially designed electronic system. The performance of the prototype was validated through experiments on a healthy human subject. Lastly, the efficacy of the prototype is demonstrated indirectly using pre-recorded EEG data from epilepsy patients. It has been observed that seizure signal can be detected via a neighboring bipolar recording configuration, which closely simulates the case of single-unit sensors for the detection of NCS and NCSE. The results of series of investigations conclude the feasibility and single-unit sensors in detecting epileptic EEG signals.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
25 January 2018 |
| Date Type: |
Publication |
| Defense Date: |
10 July 2017 |
| Approval Date: |
25 January 2018 |
| Submission Date: |
27 November 2017 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
132 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Brain, EEG, Electroencephalography, Epilepsy, NCS, NCSE, Single-unit sensor |
| Date Deposited: |
25 Jan 2018 21:41 |
| Last Modified: |
25 Jan 2018 21:41 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/33387 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}