Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis

Abstract Much attention has been focused on the non-invasive blood glucose monitoring for diabetics. It has been reported that diabetics’ breath includes acetone with abnormal concentrations and the concentrations rise gradually with patients’ blood glucose values. Therefore, the acetone in human breath can be used to monitor the development of diabetes. This paper investigates the potential of breath signals analysis as a way for blood glucose monitoring. We employ a specially designed chemical sensor system to collect and analyze breath samples of diabetic patients. Blood glucose values provided by blood test are collected simultaneously to evaluate the prediction results. To obtain an effective classification results, we apply a novel regression technique, SVOR, to classify the diabetes samples into four ordinal groups marked with ‘well controlled’, ‘somewhat controlled’, ‘poorly controlled’, and ‘not controlled’, respectively. The experimental results show that the accuracy to classify the diabetes samples can be up to 68.66%. The current prediction correct rates are not quite high, but the results are promising because it provides a possibility of non-invasive blood glucose measurement and monitoring.

[1]  Carlos Eduardo Ferrante do Amaral,et al.  Current development in non-invasive glucose monitoring. , 2008, Medical engineering & physics.

[2]  Dall Jl,et al.  MORE ACID/BASE DISTURBANCES. , 1963 .

[3]  Philip Drake,et al.  Real-time electronic nose based pathogen detection for respiratory intensive care patients , 2010 .

[4]  Peter J Sterk,et al.  An electronic nose in the discrimination of patients with asthma and controls. , 2007, The Journal of allergy and clinical immunology.

[5]  W. Ping,et al.  A novel method for diabetes diagnosis based on electronic nose. , 1997 .

[6]  S J Pöppl,et al.  Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. , 2002, Diabetes, nutrition & metabolism.

[7]  U. Weimar,et al.  Detection of volatile compounds correlated to human diseases through breath analysis with chemical sensors , 2002 .

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  M. Santonico,et al.  Olfactory systems for medical applications , 2008 .

[10]  Simone Meinardi,et al.  Breath ethanol and acetone as indicators of serum glucose levels: an initial report. , 2005, Diabetes technology & therapeutics.

[11]  G. Rooth,et al.  Acetone in alveolar air, and the control of diabetes. , 1966, Lancet.

[12]  Onofrio Resta,et al.  An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. , 2009, Lung cancer.

[13]  Andrea Bonarini,et al.  Lung Cancer Identification by an Electronic Nose based on an Array of MOS Sensors , 2007, 2007 International Joint Conference on Neural Networks.

[14]  D M Hansell,et al.  Elevated levels of exhaled nitric oxide in bronchiectasis. , 1995, American journal of respiratory and critical care medicine.

[15]  Nicoletta Pellegrini,et al.  Colonic fermentation of indigestible carbohydrates contributes to the second-meal effect. , 2006, The American journal of clinical nutrition.

[16]  N. Bârsan,et al.  Electronic nose: current status and future trends. , 2008, Chemical reviews.

[17]  T R Fraser,et al.  Breath acetone and blood sugar measurements in diabetes. , 1969, Clinical science.

[18]  I. Horváth,et al.  Increased levels of exhaled carbon monoxide in bronchiectasis: a new marker of oxidative stress , 1998, Thorax.

[19]  O. Balchum,et al.  STUDIES OF METABOLIC PRODUCTS IN EXPIRED AIR. I. METHANE. , 1963, The Journal of laboratory and clinical medicine.

[20]  Martin Liess,et al.  Electric-field-induced migration of chemisorbed gas molecules on a sensitive film—a new chemical sensor , 2002 .

[21]  Ke Huang,et al.  Sparse Representation for Signal Classification , 2006, NIPS.

[22]  Yuh-Jiuan Lin,et al.  Application of the electronic nose for uremia diagnosis , 2001 .

[23]  Terence H Risby,et al.  Breath biomarkers for detection of human liver diseases: preliminary study , 2002, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[24]  Q. Zhang,et al.  Diagnosis of diabetes by image detection of breath using gas-sensitive LAPS. , 2000, Biosensors & bioelectronics.

[25]  Giorgio Pennazza,et al.  An investigation on electronic nose diagnosis of lung cancer. , 2010, Lung cancer.

[26]  David Zhang,et al.  A Novel Breath Analysis System Based on Electronic Olfaction , 2010, IEEE Transactions on Biomedical Engineering.

[27]  L. Marchand,et al.  Breath hydrogen and methane in populations at different risk for colon cancer , 1993, International journal of cancer.

[28]  Wei Chu,et al.  New approaches to support vector ordinal regression , 2005, ICML.

[29]  Eugenio Baraldi,et al.  Exhaled NO and breath condensate. , 2006, Paediatric respiratory reviews.

[30]  L. Laffel.  Ketone bodies: a review of physiology, pathophysiology and application of monitoring to diabetes , 1999, Diabetes/metabolism research and reviews.

[31]  W. Maziak,et al.  Exhaled nitric oxide in chronic obstructive pulmonary disease. , 1998, American journal of respiratory and critical care medicine.

[32]  Amnon Shashua,et al.  Ranking with Large Margin Principle: Two Approaches , 2002, NIPS.

[33]  P. Španěl,et al.  Quantitative analysis of ammonia on the breath of patients in end-stage renal failure. , 1997, Kidney international.

[34]  M. Shepherd,et al.  A Study on Breath Acetone in Diabetic Patients Using a Cavity Ringdown Breath Analyzer: Exploring Correlations of Breath Acetone With Blood Glucose and Glycohemoglobin A1C , 2010, IEEE Sensors Journal.

[35]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[36]  A. Maran,et al.  Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria. , 2007, Diabetes research and clinical practice.

[37]  M. J. Sulway,et al.  Acetone in diabetic ketoacidosis. , 1970, Lancet.