NDLI: Blood glucose prediction by breath analysis system with feature selection and model fusion

Content Provider	IEEE Xplore Digital Library
Author	Ke Yan Zhang, D.
Copyright Year	2014
Description	Author affiliation: Shenzhen Grad. Sch., Harbin Inst. of Technol., Shenzhen, China (Zhang, D.) \|\| Dept. of Electron. Eng., Tsinghua Univ., Shenzhen, China (Ke Yan)
Abstract	It has been shown that the concentration of acetone in breath is correlated with the subject's blood glucose level (BGL). Therefore, noninvasive BGL monitoring of diabetics can be achieved by the analysis of components in breath. In this paper, a breath analysis device with 10 gas sensors is designed to measure breath samples. Transient features are extracted from the signals of the sensors. Sequential forward selection is applied on the features to find the most informative ones. In order to reduce the interference brought by the inter-subject variance of breath acetone, global and local BGL prediction models are built and fused. The two models are based on different training strategies and have different advantages. Experiments were conducted using 203 breath samples from 36 diabetic subjects. Results show that the accuracy of the proposed feature is better than other similar features and the model fusion strategy is effective. The mean absolute error and mean relative absolute error of the system are 2.07 mmol/L and 20.69%, respectively.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	6406
Ending Page	6409
File Size	770494
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6945094
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensors Transient analysis Diabetes Feature extraction Accuracy Predictive models Blood
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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