Utilizing Brain Activity to Non-Invasively Predict Blood Glucose Levels

Simple item page
dc.access.optionOpen Access
dc.contributor.advisorCastles, Ricky T.
dc.contributor.authorCranwell, Bryce A.
dc.contributor.departmentEngineering
dc.date.accessioned2017-01-11T22:15:31Z
dc.date.available2018-01-23T17:31:57Z
dc.date.created2016-12
dc.date.issued2016-12-16
dc.date.submittedDecember 2016
dc.date.updated2017-01-11T14:33:25Z
dc.degree.departmentEngineering
dc.degree.disciplineMS-Biomedical Engineering
dc.degree.grantorEast Carolina University
dc.degree.levelMasters
dc.degree.nameM.S.
dc.description.abstractDiabetes Mellitus is a chronic condition that must be routinely self-monitored to prevent life threatening complications, diabetic coma, and to mitigate long-term complications, such as neuropathy and kidney failure. Despite these risks, many people with diabetes do not properly self-monitor. An often cited reason for not properly self-monitoring is the discomfort associated with the lancing procedure. To alleviate the need for finger-sticks, this research has focused on a non-invasive blood glucose monitoring technique by combining electroencephalography (EEG) with artificial neural networks (ANN). Eleven volunteers participated in three, hour-long, recording sessions. Not all volunteers completed all sessions. During this time the volunteer's blood glucose level (BGL) was monitored in fifteen-minute increments, with the exception of the fifteenth minute. At fifteen minutes into the data collection, the volunteers drank a 52g carbohydrate load. EEG data was recorded continuously over the data collection period. From the EEG data, frequency features were extracted and used to train an ANN that would predict the users BGL based upon EEG input. This data was used to determine which channels that correlated most strongly with glucose levels, determined the optimal number of neurons used for the training algorithm, and finally trained/tested an ANN. The channels that corresponded most consistently and strongly are channels AF4, F4, F8, FC5, O1, O2, and P8. The optimal number of neurons for the network was determined to be 99 neurons. Ultimately, the method of training an ANN to non-invasively predict BGLs has moderate success, but needs more research to be conclusive.
dc.embargo.lift2018-01-11
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10342/6046
dc.language.isoen
dc.publisherEast Carolina University
dc.subjectBrain Activity
dc.subjectBrain Metabolism
dc.subject.lcshBlood sugar monitoring--Technological innovations
dc.subject.lcshDiabetes
dc.subject.lcshElectroencephalography
dc.subject.lcshNeural networks (Neurobiology)
dc.subject.lcshMachine learning
dc.titleUtilizing Brain Activity to Non-Invasively Predict Blood Glucose Levels
dc.typeMaster's Thesis
dc.type.materialtext

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
CRANWELL-MASTERSTHESIS-2016.pdf
Size:
6.24 MB
Format:
Adobe Portable Document Format
Download

Collections

Master's Theses
Engineering