| Title: | Recent Advancements and Future Prospects on E-Nose Sensors Technology and Machine Learning Approaches for Non-Invasive Diabetes Diagnosis: A Review |
| DOI: | 10.1109/RBME.2020.2993591 |
| ISSN/ISBN: | 1941-1189 (Electronic) 1937-3333 (Linking) |
| 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" |
| Keywords: | "Acetone/analysis Biomarkers/analysis Biosensing Techniques *Breath Tests *Diabetes Mellitus/diagnosis/metabolism *Electronic Nose Glucose/analysis/metabolism Humans *Machine Learning *Monitoring, Physiologic Signal Processing, Computer-Assisted;" |
| Notes: | "MedlineLekha, S M, Suchetha eng Review 2020/05/13 IEEE Rev Biomed Eng. 2021; 14:127-138. doi: 10.1109/RBME.2020.2993591. Epub 2021 Jan 22" |
