Diabetes identification and classification by means of a breath analysis system

  • Authors:

  • Dongmin Guo;David Zhang;Naimin Li;Lei Zhang;Jianhua Yang

  • Affiliations:

  • Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computer Science and Engineering, HIT, Harbin, P.R. China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong;School of Automation, Northwestern Polytechnical University, Xian, P.R. China

  • Venue:
  • ICMB'10 Proceedings of the Second international conference on Medical Biometrics
  • Year:
  • 2010

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Abstract

This article proposes a breath analysis system that makes use of chemical sensors to detect acetone in human breath, and hence detect the diabetes and measure the blood glucose levels of diabetics. We captured the breath samples from healthy persons and patients known to be afflicted with diabetes and conducted experiments on disease identification and simultaneous blood glucose measurement. SVM classifier was used to identify diabetes from healthy samples and three models were built to fit the curves that can represent the blood glucose levels. The results show that the system is not only able to distinguish between breath samples from patients with diabetes and healthy subjects, but also to represent the fluctuation of blood sugar of diabetics and therefore to be an evaluation tool for monitoring the blood glucose of diabetes.