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

Abstract

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.

Recent developments in minimally and truly non-invasive blood glucose monitoring techniques

“…A sensor based on the combination of impedance (10 to 76 kHz range) and multiwavelength (850nm, 950nm, 1300nm) near infra-red (NIR) spectroscopy with a dedicated integrated circuit [15] and a technique measuring the refraction of visible laser light to overcome the signal to noise ratio issue related to NIR spectroscopy [16] were also reported. Wearable sensors capable of measuring glucose concentration through saliva and sweat [17] [18], and a benchtop system capable of estimating glucose level through breath have been published [19] [20].…”

Improving the transfer ability of prediction models for electronic noses

“…In our previous work, we introduced a portable e-nose specialized for breath analysis [7,29]. It is equipped with an array of 11 sensors.…”

“…Electronic noses (e-noses) have become effective tools in many areas, such as air quality monitoring [1,2], quality control of food [3], and clinical analysis [4][5][6][7]. As increasing number of e-nose systems are being deployed in real-life applications, the problem of calibration transfer is receiving more and more attention.…”

“…In our previous work, we introduced a portable e-nose specialized for breath analysis [7]. It achieves disease screening and monitoring through analyzing the biomarkers in breath, such as acetone, hydrogen, and ammonia.…”

A Novel Medical E-Nose Signal Analysis System

“…Although gas chromatography (GC) has been proven to be effective in breath diagnosis [19], using e-nose instead of GC [20] to analysis human breath is generally cheaper, faster, more portable, and easier to operate [21].…”