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: Ramji Kalidoss, Snekhalatha Umapathy, Radhakrishnan Kothalam and Uthvag Sakthivelu Source Type: research