Diabetes identification and classification by means of a breath analysis system

Dongmin Guo; David Zhang; Naimin Li; Lei Zhang; Jianhua Yang

doi:10.1007/978-3-642-13923-9_6

Diabetes identification and classification by means of a breath analysis system

Dongmin Guo
, David Zhang
, Naimin Li
, Lei Zhang
, Jianhua Yang

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

This article proposes a breath analysis system that makes use of chemical sensors to detect acetone in human breath, and hence detect the diabetes and measure the blood glucose levels of diabetics. We captured the breath samples from healthy persons and patients known to be afflicted with diabetes and conducted experiments on disease identification and simultaneous blood glucose measurement. SVM classifier was used to identify diabetes from healthy samples and three models were built to fit the curves that can represent the blood glucose levels. The results show that the system is not only able to distinguish between breath samples from patients with diabetes and healthy subjects, but also to represent the fluctuation of blood sugar of diabetics and therefore to be an evaluation tool for monitoring the blood glucose of diabetes.

Original language	English
Title of host publication	Medical Biometrics - Second International Conference, ICMB 2010, Proceedings
Pages	52-63
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-13923-9_6
State	Published - 2010
Event	2nd International Conference on Medical Biometrics, ICMB 2010 - Hong Kong, China Duration: 28 Jun 2010 → 30 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6165 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Medical Biometrics, ICMB 2010
Country/Territory	China
City	Hong Kong
Period	28/06/10 → 30/06/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1007/978-3-642-13923-9_6

Cite this

Guo, D., Zhang, D., Li, N., Zhang, L., & Yang, J. (2010). Diabetes identification and classification by means of a breath analysis system. In Medical Biometrics - Second International Conference, ICMB 2010, Proceedings (pp. 52-63). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6165 LNCS). https://doi.org/10.1007/978-3-642-13923-9_6

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title = "Diabetes identification and classification by means of a breath analysis system",

abstract = "This article proposes a breath analysis system that makes use of chemical sensors to detect acetone in human breath, and hence detect the diabetes and measure the blood glucose levels of diabetics. We captured the breath samples from healthy persons and patients known to be afflicted with diabetes and conducted experiments on disease identification and simultaneous blood glucose measurement. SVM classifier was used to identify diabetes from healthy samples and three models were built to fit the curves that can represent the blood glucose levels. The results show that the system is not only able to distinguish between breath samples from patients with diabetes and healthy subjects, but also to represent the fluctuation of blood sugar of diabetics and therefore to be an evaluation tool for monitoring the blood glucose of diabetes.",

author = "Dongmin Guo and David Zhang and Naimin Li and Lei Zhang and Jianhua Yang",

year = "2010",

doi = "10.1007/978-3-642-13923-9\_6",

language = "英语",

isbn = "3642139221",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "52--63",

booktitle = "Medical Biometrics - Second International Conference, ICMB 2010, Proceedings",

note = "2nd International Conference on Medical Biometrics, ICMB 2010 ; Conference date: 28-06-2010 Through 30-06-2010",

}

Guo, D, Zhang, D, Li, N, Zhang, L & Yang, J 2010, Diabetes identification and classification by means of a breath analysis system. in Medical Biometrics - Second International Conference, ICMB 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6165 LNCS, pp. 52-63, 2nd International Conference on Medical Biometrics, ICMB 2010, Hong Kong, China, 28/06/10. https://doi.org/10.1007/978-3-642-13923-9_6

Diabetes identification and classification by means of a breath analysis system. / Guo, Dongmin; Zhang, David; Li, Naimin et al.
Medical Biometrics - Second International Conference, ICMB 2010, Proceedings. 2010. p. 52-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6165 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Zhang, David

AU - Li, Naimin

AU - Zhang, Lei

AU - Yang, Jianhua

PY - 2010

Y1 - 2010

N2 - This article proposes a breath analysis system that makes use of chemical sensors to detect acetone in human breath, and hence detect the diabetes and measure the blood glucose levels of diabetics. We captured the breath samples from healthy persons and patients known to be afflicted with diabetes and conducted experiments on disease identification and simultaneous blood glucose measurement. SVM classifier was used to identify diabetes from healthy samples and three models were built to fit the curves that can represent the blood glucose levels. The results show that the system is not only able to distinguish between breath samples from patients with diabetes and healthy subjects, but also to represent the fluctuation of blood sugar of diabetics and therefore to be an evaluation tool for monitoring the blood glucose of diabetes.

AB - This article proposes a breath analysis system that makes use of chemical sensors to detect acetone in human breath, and hence detect the diabetes and measure the blood glucose levels of diabetics. We captured the breath samples from healthy persons and patients known to be afflicted with diabetes and conducted experiments on disease identification and simultaneous blood glucose measurement. SVM classifier was used to identify diabetes from healthy samples and three models were built to fit the curves that can represent the blood glucose levels. The results show that the system is not only able to distinguish between breath samples from patients with diabetes and healthy subjects, but also to represent the fluctuation of blood sugar of diabetics and therefore to be an evaluation tool for monitoring the blood glucose of diabetes.

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DO - 10.1007/978-3-642-13923-9_6

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SN - 9783642139222

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Biometrics - Second International Conference, ICMB 2010, Proceedings

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ER -

Guo D, Zhang D, Li N, Zhang L, Yang J. Diabetes identification and classification by means of a breath analysis system. In Medical Biometrics - Second International Conference, ICMB 2010, Proceedings. 2010. p. 52-63. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-13923-9_6

Diabetes identification and classification by means of a breath analysis system

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