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

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Breath Analysis for Medical Applications

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.

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References

  • 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: Chem

    Google Scholar 

  • Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167

    Article  Google Scholar 

  • Cao W, Duan Y (2007) Current status of methods and techniques for breath analysis. Crit Rev Anal Chem 37:3–13

    Article  Google Scholar 

  • Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2:27

    Google Scholar 

  • 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 810:269–275

    Article  Google Scholar 

  • 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:CR366

    Google Scholar 

  • 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 7:115–123

    Article  Google Scholar 

  • Ghimenti S, Tabucchi S, Lomonaco T et al (2013) Monitoring breath during oral glucose tolerance tests. J Breath Res 7:017115

    Article  Google Scholar 

  • 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 12:455–463

    Article  Google Scholar 

  • Guo D, Zhang D, Li N et al (2010) A novel breath analysis system based on electronic olfaction. IEEE Trans Biomed Eng 57:2753–2763

    Article  Google Scholar 

  • 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 173:106–113

    Article  Google Scholar 

  • Gutierrez-Osuna R, Gutierrez-Galvez A, Powar N (2003) Transient response analysis for temperature-modulated chemoresistors. Sens Actuators B: Chem 93:57–66

    Article  Google Scholar 

  • Hierlemann A, Gutierrez-Osuna R (2008) Higher-order chemical sensing. Chem Rev 108:563–613

    Article  Google Scholar 

  • 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 22:035201

    Article  Google Scholar 

  • 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 154–158

    Google Scholar 

  • 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 107:155–160

    Article  Google Scholar 

  • Minh TDC, Blake DR, Galassetti PR (2012) The clinical potential of exhaled breath analysis for diabetes mellitus. Diabetes Res Clin Pract 97:195–205

    Article  Google Scholar 

  • 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 104:15613–15618

    Google Scholar 

  • 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 116:1007–1011

    Article  Google Scholar 

  • Phillips M, Cataneo RN, Cheema T et al (2004) Increased breath biomarkers of oxidative stress in diabetes mellitus. Clin Chim Acta 344:189–194

    Article  Google Scholar 

  • Ramachandran A, Moses A, Shetty S et al (2010) A new non-invasive technology to screen for dysglycaemia including diabetes. Diabetes Res Clin Pract 88:302–306

    Article  Google Scholar 

  • 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 7:037110

    Article  Google Scholar 

  • Risby TH, Solga S (2006) Current status of clinical breath analysis. Appl Phys B 85:421–426

    Article  Google Scholar 

  • 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 25:275–278

    Article  Google Scholar 

  • 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 13:4229–4235

    Article  Google Scholar 

  • Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222

    Article  MathSciNet  Google Scholar 

  • Španěl P, Dryahina K, Rejšková A et al (2011) Breath acetone concentration; biological variability and the influence of diet. Physiol Meas 32:N23

    Article  Google Scholar 

  • 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 57:2884–2890

    Article  Google Scholar 

  • Turner C (2011) Potential of breath and skin analysis for monitoring blood glucose concentration in diabetes. Expert Rev Mol Diagn 11:497–503

    Article  Google Scholar 

  • 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 3:046004

    Article  Google Scholar 

  • 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 877:2551–2556

    Article  Google Scholar 

  • Vashist SK (2012) Non-invasive glucose monitoring technology in diabetes management: a review. Anal Chim Acta 750:16–27

    Article  Google Scholar 

  • 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 10:54–63

    Article  Google Scholar 

  • Wang P, Tan Y, Xie H et al (1997) A novel method for diabetes diagnosis based on electronic nose. Biosens Bioelectron 12:1031–1036

    Article  Google Scholar 

  • 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 115:322–329

    Article  Google Scholar 

  • Yan K, Kou L, Zhang D (2017) Learning domain-invariant subspace using domain features and independence maximization. IEEE Trans Cybern

    Google Scholar 

  • Yan K, Zhang D (2012) A novel breath analysis system for diabetes diagnosis. In: 2012 international conference on computerized healthcare. Hong Kong, China, pp 166–170

    Google Scholar 

  • Yan K, Zhang D (2014) Sensor evaluation in a breath analysis system. In: 2014 international conference on medical biometrics (ICMB). IEEE, Shenzhen, pp 35–40

    Google Scholar 

  • Yan K, Zhang D (2015) Improving the transfer ability of prediction models for electronic noses. Sens Actuators B: Chem 220:115–124

    Article  Google Scholar 

  • Yan K, Zhang D (2016a) Calibration transfer and drift compensation of e-noses via coupled task learning. Sens Actuators B: Chem 225:288–297

    Article  Google Scholar 

  • Yan K, Zhang D (2016b) Correcting instrumental variation and time-varying drift: a transfer learning approach with autoencoders. IEEE Trans Instrum Meas 65:2012–2022

    Article  Google Scholar 

  • 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 108:305–308

    Article  Google Scholar 

  • Zhang Q, Wang P, Li J et al (2000) Diagnosis of diabetes by image detection of breath using gas-sensitive laps. Biosens Bioelectron 15:249–256

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Biometrics Research Centre, The Hong Kong Polytechnic University, Hong Kong, China

    David Zhang

  2. Wake Forest University, Winston-Salem, NC, USA

    Dongmin Guo

  3. National Institute of Health, Bethesda, USA

    Ke Yan

Authors
  1. David Zhang
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  2. Dongmin Guo
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  3. Ke Yan
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Correspondence to David Zhang .

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© 2017 Springer Nature Singapore Pte Ltd.

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Zhang, D., Guo, D., Yan, K. (2017). A Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction. In: Breath Analysis for Medical Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-4322-2_14

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  • DOI: https://doi.org/10.1007/978-981-10-4322-2_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4321-5

  • Online ISBN: 978-981-10-4322-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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