Electronic Nose System Based on Quartz Crystal Microbalance Sensor for Blood Glucose and HbA1c Levels From Exhaled Breath Odor

Saraoğlu, Hamdi Melih; Selvi, Ali Osman; Ebeoğlu, Mehmet Ali; Taşaltın, Cihat

doi:10.1109/jsen.2013.2265233

Cited by 50 publications

(37 citation statements)

References 23 publications

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“…The MAE, MRAE, and correlation coefficient of the prediction are 2.18, 21.7%, and 0.641, respectively. The result is better than a latest study [20], in which a chemical sensor system was designed to predict the BGL of 30 samples with MRAE = 25.24%.…”

Section: ) Blood Glucose Level and Hba1c Predictionmentioning

confidence: 71%

“…The author of [1] concluded that calibration of acetone with BGL for each individual is required. However, in previous breath analysis systems aiming at predicting BGL [19], [20], the prediction models are not subject-specific.…”

Section: Development Of Subject-specific Prediction Modelsmentioning

confidence: 99%

“…A few chemical sensor systems have been developed for either diabetes diagnosis [15]- [18] or BGL prediction [19], [20]. However, there are still problems not solved in these systems.…”

Section: Introductionmentioning

confidence: 99%

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Design of a Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction

Yan

Zhang

et al. 2014

IEEE Trans. Biomed. Eng.

113

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It has been reported that concentrations of several biomarkers in diabetics' breath show significant difference from those in healthy people's breath. Concentrations of some biomarkers are also correlated with the blood glucose levels (BGLs) of diabetics. Therefore, it is possible to screen for diabetes and predict BGLs by analyzing one's breath. In this paper, we describe the design of a novel breath analysis system for this purpose. The system uses carefully selected chemical sensors to detect biomarkers in breath. Common interferential factors, including humidity and the ratio of alveolar air in breath, are compensated or handled in the algorithm. Considering the intersubject variance of the components in breath, we build subject-specific prediction models to improve the accuracy of BGL prediction. A total of 295 breath samples from healthy subjects and 279 samples from diabetic subjects were collected to evaluate the performance of the system. The sensitivity and specificity of diabetes screening are 91.51% and 90.77%, respectively. The mean relative absolute error for BGL prediction is 21.7%. Experiments show that the system is effective and that the strategies adopted in the system can improve its accuracy. The system potentially provides a noninvasive and convenient method for diabetes screening and BGL monitoring as an adjunct to the standard criteria.

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Section: ) Blood Glucose Level and Hba1c Predictionmentioning

confidence: 71%

Section: Development Of Subject-specific Prediction Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction

Yan

Zhang

et al. 2014

IEEE Trans. Biomed. Eng.

113

View full text Add to dashboard Cite

show abstract

“…Flow-injection sensors [51], disposable thick-film sensors [52] and core-shell NP-modified electrodes [53] have been reported for FV determination. In addition, various types of HbA1c sensors have been developed based on surface-plasmon resonance [54], impedance spectroscopy [55,56], field-effect transistors [57,58], molecularly-imprinted polymers [59][60][61], and quartzcrystal microbalance [62,63].…”

Section: Biosensors Based On Ferrocene-boronic Acid (Fcba)mentioning

confidence: 99%