Advertisement

A Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction

  • David ZhangEmail author
  • Dongmin Guo
  • Ke Yan
Chapter

Abstract

It has been reported that concentrations of several biomarkers in diabetics’ breath show significant difference from those in healthy people’s breath. Concentrations of some biomarkers are also correlated with the blood glucose levels (BGLs) of diabetics. Therefore, it is possible to screen for diabetes and predict BGLs by analyzing one’s breath. In this chapter, we describe the design of a novel optimized breath analysis system for this purpose. The system uses carefully selected chemical sensors to detect biomarkers in breath. Common interferential factors, including humidity and the ratio of alveolar air in breath, are compensated or handled in the algorithm. Considering the inter-subject variance of the components in breath, we design a feature augmentation strategy to learn subject-specific prediction models to improve the accuracy of BGL prediction. 295 breath samples from healthy subjects and 279 samples from diabetic subjects were collected to evaluate the performance of the system. The sensitivity and specificity of diabetes screening are 91.51% and 90.77%, respectively. The mean relative absolute error for BGL prediction is 20.6%. Experiments show that the system is effective and that the strategies adopted in the system can improve its accuracy. The system potentially provides a noninvasive and convenient method for diabetes screening and BGL monitoring as an adjunct to the standard criteria.

Keywords

Alveolar air Blood glucose level Diabetes screening Humidity compensation Inter-subject variance 

References

  1. Amini A, Bagheri MA, Montazer G (2012) Improving gas identification accuracy of a temperature-modulated gas sensor using an ensemble of classifiers. Sens Actuators B: ChemGoogle Scholar
  2. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167CrossRefGoogle Scholar
  3. Cao W, Duan Y (2007) Current status of methods and techniques for breath analysis. Crit Rev Anal Chem 37:3–13CrossRefGoogle Scholar
  4. Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2:27Google Scholar
  5. Deng C, Zhang J, Yu X et al (2004) Determination of acetone in human breath by gas chromatography-mass spectrometry and solid-phase microextraction with on-fiber derivatization. J Chromatogr B 810:269–275CrossRefGoogle Scholar
  6. Di Natale C, Paolesse R, D’arcangelo G et al (2005) Identification of schizophrenic patients by examination of body odor using gas chromatography-mass spectrometry and a cross-selective gas sensor array. Med Sci Monit: Int Med J Exp Clin Res 11:CR366Google Scholar
  7. Galassetti PR, Novak B, Nemet D et al (2005) Breath ethanol and acetone as indicators of serum glucose levels: an initial report. Diabetes Technol Ther 7:115–123CrossRefGoogle Scholar
  8. Ghimenti S, Tabucchi S, Lomonaco T et al (2013) Monitoring breath during oral glucose tolerance tests. J Breath Res 7:017115CrossRefGoogle Scholar
  9. Greiter M, Keck L, Siegmund T et al (2010) Differences in exhaled gas profiles between patients with type 2 diabetes and healthy controls. Diabetes Technol Ther 12:455–463CrossRefGoogle Scholar
  10. Guo D, Zhang D, Li N et al (2010) A novel breath analysis system based on electronic olfaction. IEEE Trans Biomed Eng 57:2753–2763CrossRefGoogle Scholar
  11. Guo D, Zhang D, Zhang L et al (2012) Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B: Chem 173:106–113CrossRefGoogle Scholar
  12. Gutierrez-Osuna R, Gutierrez-Galvez A, Powar N (2003) Transient response analysis for temperature-modulated chemoresistors. Sens Actuators B: Chem 93:57–66CrossRefGoogle Scholar
  13. Hierlemann A, Gutierrez-Osuna R (2008) Higher-order chemical sensing. Chem Rev 108:563–613CrossRefGoogle Scholar
  14. Hosseini-Golgoo S, Hossein-Babaei F (2011) Assessing the diagnostic information in the response patterns of a temperature-modulated tin oxide gas sensor. Meas Sci Technol 22:035201CrossRefGoogle Scholar
  15. Kashwan K, Bhuyan M (2005) Robust electronic-nose system with temperature and humidity drift compensation for tea and spice flavour discrimination. In: 2005 Asian conference on sensors and the international conference on new techniques in pharmaceutical and biomedical research. IEEE, pp 154–158Google Scholar
  16. Lee J, Ngo J, Blake D et al (2009) Improved predictive models for plasma glucose estimation from multi-linear regression analysis of exhaled volatile organic compounds. J Appl Physiol 107:155–160CrossRefGoogle Scholar
  17. Minh TDC, Blake DR, Galassetti PR (2012) The clinical potential of exhaled breath analysis for diabetes mellitus. Diabetes Res Clin Pract 97:195–205CrossRefGoogle Scholar
  18. Novak B, Blake D, Meinardi S et al (2007) Exhaled methyl nitrate as a noninvasive marker of hyperglycemia in type 1 diabetes. Proc Nat Acad Sci 104:15613–15618Google Scholar
  19. Paredi P, Biernacki W, Invernizzi G et al (1999) Exhaled carbon monoxide levels elevated in diabetes and correlated with glucose concentration in blood: a new test for monitoring the disease? Chest 116:1007–1011CrossRefGoogle Scholar
  20. Phillips M, Cataneo RN, Cheema T et al (2004) Increased breath biomarkers of oxidative stress in diabetes mellitus. Clin Chim Acta 344:189–194CrossRefGoogle Scholar
  21. Ramachandran A, Moses A, Shetty S et al (2010) A new non-invasive technology to screen for dysglycaemia including diabetes. Diabetes Res Clin Pract 88:302–306CrossRefGoogle Scholar
  22. Righettoni M, Schmid A, Amann A et al (2013) Correlations between blood glucose and breath components from portable gas sensors and ptr-tof-ms. J Breath Res 7:037110CrossRefGoogle Scholar
  23. Risby TH, Solga S (2006) Current status of clinical breath analysis. Appl Phys B 85:421–426CrossRefGoogle Scholar
  24. Rohlfing CL, Wiedmeyer H-M, Little RR et al (2002) Defining the relationship between plasma glucose and HbA1c analysis of glucose profiles and HbA1c in the diabetes control and complications trial. Diabetes Care 25:275–278CrossRefGoogle Scholar
  25. Saraoğlu HM, Selvi AO, Ebeoğlu MA et al (2013) Electronic nose system based on quartz crystal microbalance sensor for blood glucose and HbA1c levels from exhaled breath odor. IEEE Sens J 13:4229–4235CrossRefGoogle Scholar
  26. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222MathSciNetCrossRefGoogle Scholar
  27. Španěl P, Dryahina K, Rejšková A et al (2011) Breath acetone concentration; biological variability and the influence of diet. Physiol Meas 32:N23CrossRefGoogle Scholar
  28. Trincavelli M, Coradeschi S, Loutfi A et al (2010) Direct identification of bacteria in blood culture samples using an electronic nose. IEEE Trans Biomed Eng 57:2884–2890CrossRefGoogle Scholar
  29. Turner C (2011) Potential of breath and skin analysis for monitoring blood glucose concentration in diabetes. Expert Rev Mol Diagn 11:497–503CrossRefGoogle Scholar
  30. Turner C, Walton C, Hoashi S et al (2009) Breath acetone concentration decreases with blood glucose concentration in type i diabetes mellitus patients during hypoglycaemic clamps. J Breath Res 3:046004CrossRefGoogle Scholar
  31. Ueta I, Saito Y, Hosoe M et al (2009) Breath acetone analysis with miniaturized sample preparation device: in-needle preconcentration and subsequent determination by gas chromatography–mass spectroscopy. J Chromatogr B 877:2551–2556CrossRefGoogle Scholar
  32. Vashist SK (2012) Non-invasive glucose monitoring technology in diabetes management: a review. Anal Chim Acta 750:16–27CrossRefGoogle Scholar
  33. Wang C, Mbi A, Shepherd M (2010) 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. IEEE Sens J 10:54–63CrossRefGoogle Scholar
  34. Wang P, Tan Y, Xie H et al (1997) A novel method for diabetes diagnosis based on electronic nose. Biosens Bioelectron 12:1031–1036CrossRefGoogle Scholar
  35. Wolfrum EJ, Meglen RM, Peterson D et al (2006) Metal oxide sensor arrays for the detection, differentiation, and quantification of volatile organic compounds at sub-parts-per-million concentration levels. Sens Actuators B: Chem 115:322–329CrossRefGoogle Scholar
  36. Yan K, Kou L, Zhang D (2017) Learning domain-invariant subspace using domain features and independence maximization. IEEE Trans CybernGoogle Scholar
  37. Yan K, Zhang D (2012) A novel breath analysis system for diabetes diagnosis. In: 2012 international conference on computerized healthcare. Hong Kong, China, pp 166–170Google Scholar
  38. Yan K, Zhang D (2014) Sensor evaluation in a breath analysis system. In: 2014 international conference on medical biometrics (ICMB). IEEE, Shenzhen, pp 35–40Google Scholar
  39. Yan K, Zhang D (2015) Improving the transfer ability of prediction models for electronic noses. Sens Actuators B: Chem 220:115–124CrossRefGoogle Scholar
  40. Yan K, Zhang D (2016a) Calibration transfer and drift compensation of e-noses via coupled task learning. Sens Actuators B: Chem 225:288–297CrossRefGoogle Scholar
  41. Yan K, Zhang D (2016b) Correcting instrumental variation and time-varying drift: a transfer learning approach with autoencoders. IEEE Trans Instrum Meas 65:2012–2022CrossRefGoogle Scholar
  42. Yu J-B, Byun H-G, So M-S et al (2005) Analysis of diabetic patient’s breath with conducting polymer sensor array. Sens Actuators B: Chem 108:305–308CrossRefGoogle Scholar
  43. Zhang Q, Wang P, Li J et al (2000) Diagnosis of diabetes by image detection of breath using gas-sensitive laps. Biosens Bioelectron 15:249–256CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Biometrics Research CentreThe Hong Kong Polytechnic UniversityHong KongChina
  2. 2.Wake Forest UniversityWinston-SalemUSA
  3. 3.National Institute of HealthBethesdaUSA

Personalised recommendations