Non-invasive blood glucose monitoring using CO2 breath analyzer
Diabetes, a metabolic disease that is characterized by abnormally high levels of blood glucose levels, has a 6% of the world’s attributed death causes. Catering to this disease requires constant monitoring of blood sugar. However, invasive processes are the most prevalent method of monitoring and ha...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2022
|
| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etdb_physics/8 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1005&context=etdb_physics |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | De La Salle University |
| Language: | English |
| Summary: | Diabetes, a metabolic disease that is characterized by abnormally high levels of blood glucose levels, has a 6% of the world’s attributed death causes. Catering to this disease requires constant monitoring of blood sugar. However, invasive processes are the most prevalent method of monitoring and have inevitably caused discomfort and fear of psychological anticipation of pain. Non-invasive methods are being developed as a better alternative for blood glucose monitoring and to do so, this study aims to utilize exhaled carbon dioxide concentration as a means to monitor blood sugar levels, in such a way that a correlation between exhaled carbon dioxide and blood sugar levels is established and a calibration curve is generated. The methodology of the study required 12 participants, including, non-diabetics, pre-diabetics, and diabetics. Their blood sugar levels were monitored using the conventional finger-prick method and the exhaled carbon dioxide concentration was measured using the CDM7160-C00 carbon dioxide sensor, at which they breathe for 1 minute, during 3 time periods. Analysis of the data required the Linear Regression Analysis. Results show that there is a linear relationship between exhaled carbon dioxide concentration and blood glucose level, with a negative regression coefficient. The derived linear regression equation is 𝑦 = − 0. 0076𝑥 + 357. 6, where y is the blood glucose level in mg/dL and x is the carbon dioxide concentration in ppm. This indicates that exhaled carbon dioxide decreases as blood glucose levels rise. The housing and sensor can also be used to estimate blood glucose levels. |
|---|