Adsorption kinetics feature extraction from breathprint obtained by graphene based sensors for diabetes diagnosis

The correlation between blood glucose and breath acetone suggested by several studies has spurred the research community to develop an electronic (e-nose) for diabetes diagnosis. Herein, we have validated the in-house graphene based sensors with known acetone concentration. The sensor performances such as sensitivity, selectivity and stability (SSS) suggested their potential use in acquiring breath print. The 10% higher mean saturation voltage for 30 diabetic subjects ensured a discrimination accuracy of 65% with a positive correlation ( r = 0.88) between biochemically measured and non-invasively estimated (glycated haemoglobin) HbA1c. For the improvement of classification rate, thirteen features associated with the adsorption kinetics were extracted from the breathprint from each of the three sensors. The features given as an input to the Na ïve Bayes classification model fetched an accuracy of 68.33%. Elimination of redundant features by distinction index and one-R featur...
Source: Journal of Breath Research - Category: Respiratory Medicine Authors: Source Type: research