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

Dongmin Guo, Dapeng Zhang, Lei Zhang, Guangming Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

83 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)106-113
Number of pages8
JournalSensors and Actuators, B: Chemical
Publication statusPublished - 1 Oct 2012


  • Blood glucose levels
  • Breath analysis
  • Diabetes detection
  • Probabilistic output
  • Support vector ordinal regression

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Electrical and Electronic Engineering
  • Materials Chemistry


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