968-P: Volatile Organic Compounds in Breath Predict Hypoglycemia Well Before Plasma Glucose Levels Fall

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 968-P
Author(s):  
AMANDA P. SIEGEL ◽  
ALI DANESHKHAH ◽  
KIEREN J. MATHER ◽  
MANGILAL AGARWAL
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Plasma Glucose ◽  
Glucose Levels ◽  
Volatile Organic

scholarly journals Improved predictive models for plasma glucose estimation from multi-linear regression analysis of exhaled volatile organic compounds

2009 ◽  
Vol 107 (1) ◽  
pp. 155-160 ◽  
Author(s):  
Jane Lee ◽  
Jerry Ngo ◽  
Don Blake ◽  
Simone Meinardi ◽  
Andria M. Pontello ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Linear Regression ◽  
Organic Compounds ◽  
Plasma Glucose ◽  
Linear Regression Analysis ◽  
Intravenous Glucose ◽  
Glucose Testing ◽  
Glucose Ingestion ◽  
Volatile Organic ◽  
Multi Linear Regression

Exhaled volatile organic compounds (VOCs) represent ideal biomarkers of endogenous metabolism and could be used to noninvasively measure circulating variables, including plasma glucose. We previously demonstrated that hyperglycemia in different metabolic settings (glucose ingestion in pediatric Type 1 diabetes) is paralleled by changes in exhaled ethanol, acetone, and methyl nitrate. In this study we integrated these gas changes along with three additional VOCs (2 forms of xylene and ethylbenzene) into multi-linear regression models to predict plasma glucose profiles in 10 healthy young adults, during the 2 h following an intravenous glucose bolus (matched samples of blood, exhaled and room air were collected at 12 separate time points). The four-gas model with highest predictive accuracy estimated plasma glucose in each subject with a mean R value of 0.91 (range 0.70–0.98); increasing the number of VOCs in the model only marginally improved predictions (average R with best 5-gas model = 0.93; with 6-gas model = 0.95). While practical development of this methodology into clinically usable devices will require optimization of predictive algorithms on large-scale populations, our data prove the feasibility and potential accuracy of breath-based glucose testing.

scholarly journals Microcontroller Implementation of Support Vector Machine for Detecting Blood Glucose Levels Using Breath Volatile Organic Compounds

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2283 ◽  
Author(s):  
Matthew Boubin ◽  
Sudhir Shrestha
Keyword(s):  
Support Vector Machine ◽  
Blood Glucose ◽  
Volatile Organic Compounds ◽  
Embedded System ◽  
Organic Compounds ◽  
Sensor Arrays ◽  
Support Vector ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Volatile Organic

This paper presents an embedded system-based solution for sensor arrays to estimate blood glucose levels from volatile organic compounds (VOCs) in a patient’s breath. Support vector machine (SVM) was trained on a general-purpose computer using an existing SVM library. A training model, optimized to achieve the most accurate results, was implemented in a microcontroller with an ATMega microprocessor. Training and testing was conducted using artificial breath that mimics known VOC footprints of high and low blood glucose levels. The embedded solution was able to correctly categorize the corresponding glucose levels of the artificial breath samples with 97.1% accuracy. The presented results make a significant contribution toward the development of a portable device for detecting blood glucose levels from a patient’s breath.

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