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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 1,244-1,260, DOI: https://doi.org/10.21307/ijssis-2017-805
License : (CC BY-NC-ND 4.0)
Received Date : 01-April-2015 / Accepted: 25-April-2015 / Published Online: 01-June-2015
There has been a constant demand for the development of non-invasive, sensitive glucose sensor system that offers fast and real-time electronic readout of blood glucose levels. In this article, we propose a new system for detecting blood glucose levels by estimating the concentration of acetone in the exhaled breath. A TGS822 tin oxide (SnO2) sensor has been used to detect the concentration of acetone in the exhaled air. Acetone in exhaled breath showed a correlation with the blood glucose levels. Effects of pressure, temperature and humidity have been considered. Artificial Neural Network (ANN) has been used to extract features from the output waveform of the sensors. The system has been trained and tested with patient data in the blood glucose ranges from 80 mg/dl to 180 mg/dl. Using the proposed system, the blood glucose concentration has been estimated within an error limit of ±7.5 mg/dl.
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