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001 ocn968303118;

003 OCoLC;

005 20170424101528.0;

006 m o d ;

007 cr unu||||||||;

008 170113s2016 ncua obm 000 0 eng d;

035 a| (Sirsi) o968303118;

035 a| (OCoLC)968303118;

049 a| EREE;

090 a| RC660;

100 1 a| Cranwell, Bryce A.,e| author.?| UNAUTHORIZED;

245 10 a| Utilizing brain activity to non-invasively predict blood glucose levels /c| by Bryce A. Cranwell.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2016.;

300 a| 260 pages :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 6.24 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2016.;

500 a| Presented to the faculty of the Department of Engineering.;

500 a| Advisor: Ricky T. Castles.;

500 a| Title from PDF t.p. (viewed February 9, 2017).;

520 3 a| Diabetes 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.;

504 a| Includes bibliographical references.;

650 0 a| Blood glucose monitoringx| Technological innovations.=| ^A563986;

650 0 a| Diabetes.=| ^A6146;

650 0 a| Electroencephalography.=| ^A1408;

650 0 a| Neural networks (Neurobiology)=| ^A327955;

650 0 a| Machine learning.=| ^A145211;

653 a| Brain Activity;

653 a| Brain Metabolism;

700 1 a| Castles, Ricky T.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Engineering.=| ^A1231592;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/6046;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4604963;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4604963-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/13/2017x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4604963-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/13/2017x| ETDz| HERESOURCE;

Utilizing brain activity to non-invasively predict blood glucose levels / by Bryce A. Cranwell.

Author/creator	Cranwell, Bryce A. author.
Other author	Castles, Ricky T., degree supervisor.
Other author	East Carolina University. Department of Engineering.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2016.
Description	260 pages : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Blood glucose monitoring--Technological innovations. Diabetes. Electroencephalography. Neural networks (Neurobiology) Machine learning.

Summary	Diabetes 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.
General note	Presented to the faculty of the Department of Engineering.
General note	Advisor: Ricky T. Castles.
General note	Title from PDF t.p. (viewed February 9, 2017).
Dissertation note	M.S. East Carolina University 2016.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Utilizing brain activity to non-invasively predict blood glucose levels / by Bryce A. Cranwell.

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