Recent Advancements and Future Prospects on E-Nose Sensors Technology and Machine Learning Approaches for Non-Invasive Diabetes Diagnosis: A Review

IEEE Rev Biomed Eng. 2021:14:127-138. doi: 10.1109/RBME.2020.2993591. Epub 2021 Jan 22.

Abstract

Diabetes mellitus, commonly measured through an invasive process which although is accurate, has manifold drawbacks especially when multiple reading are required at regular intervals. Accordingly, there is a need to develop a dependable non-invasive diabetes detection technique. Recent studies have observed that other human serums such as tears, saliva, urine and breath indicate the presence of glucose in them. These parameters open quite a few ways for non-invasive blood glucose level prediction. The analysis of a persons breath poses as a good non-invasive technique to monitor the glucose levels. It is seen that in breath, there are many bio-markers and monitoring the levels of these bio-markers indicate the possibility of various chronic diseases. Among these bio-markers, acetone a volatile organic compound found in breath has shown a good correlation to the glucose levels present in blood. Therefore, by evaluating the acetone levels in breath samples it is possible to monitor diabetes non-invasively. This paper reviews the various approaches and sensory techniques used to monitor diabetes though human breath samples.

Publication types

  • Review

MeSH terms

  • Acetone / analysis
  • Biomarkers / analysis
  • Biosensing Techniques
  • Breath Tests*
  • Diabetes Mellitus* / diagnosis
  • Diabetes Mellitus* / metabolism
  • Electronic Nose*
  • Glucose / analysis
  • Glucose / metabolism
  • Humans
  • Machine Learning*
  • Monitoring, Physiologic*
  • Signal Processing, Computer-Assisted

Substances

  • Biomarkers
  • Acetone
  • Glucose