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Conference Paper: A novel approach to estimate variations of blood glucose using noninvasive metabolic measurements
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TitleA novel approach to estimate variations of blood glucose using noninvasive metabolic measurements
 
AuthorsLee, CY1
Cheung, PYS1
Lam, KSL1
 
KeywordsBlood Glucose Variations
Classification Model
Heat Dissipation
Resting Metabolic Rate
 
Issue Date2007
 
CitationProceedings Of The 5Th Iasted International Conference On Biomedical Engineering, Biomed 2007, 2007, p. 79-84 [How to Cite?]
 
AbstractA novel method has been designed to estimate blood glucose variations noninvasively. The concept of this method is based on the glucose metabolic process in the human body. The data acquisition phase was implemented by measuring metabolic parameters including heat dissipation by conduction at the fingertip, percentage oxygen content of expired air and minute volume of expired air. A data analysis phase was performed to convert the measured parameters to variations of features, which would become input of the calibrated classification model for estimation of blood glucose variations. The classification model can be a 2-class to 5-class system showing different resolutions for the extents of blood glucose variations. In the experimental trial, a total of 190 data points were obtained from 31 normal and 159 type 2 diabetic subjects. The classification accuracy for a 3-class system was 84.26% using a linear discriminant classifier. Multiple regression analysis was also performed to compare the noninvasive method with variations of glucometer readings. The correlation coefficient was 0.88. Preliminary results show that this method has the potential to be used as blood glucose variation monitors and lifestyle educating devices for normal, pre-diabetic and type 2 diabetic persons.
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorLee, CY
 
dc.contributor.authorCheung, PYS
 
dc.contributor.authorLam, KSL
 
dc.date.accessioned2012-09-05T05:37:28Z
 
dc.date.available2012-09-05T05:37:28Z
 
dc.date.issued2007
 
dc.description.abstractA novel method has been designed to estimate blood glucose variations noninvasively. The concept of this method is based on the glucose metabolic process in the human body. The data acquisition phase was implemented by measuring metabolic parameters including heat dissipation by conduction at the fingertip, percentage oxygen content of expired air and minute volume of expired air. A data analysis phase was performed to convert the measured parameters to variations of features, which would become input of the calibrated classification model for estimation of blood glucose variations. The classification model can be a 2-class to 5-class system showing different resolutions for the extents of blood glucose variations. In the experimental trial, a total of 190 data points were obtained from 31 normal and 159 type 2 diabetic subjects. The classification accuracy for a 3-class system was 84.26% using a linear discriminant classifier. Multiple regression analysis was also performed to compare the noninvasive method with variations of glucometer readings. The correlation coefficient was 0.88. Preliminary results show that this method has the potential to be used as blood glucose variation monitors and lifestyle educating devices for normal, pre-diabetic and type 2 diabetic persons.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.identifier.citationProceedings Of The 5Th Iasted International Conference On Biomedical Engineering, Biomed 2007, 2007, p. 79-84 [How to Cite?]
 
dc.identifier.epage84
 
dc.identifier.scopuseid_2-s2.0-56149127485
 
dc.identifier.spage79
 
dc.identifier.urihttp://hdl.handle.net/10722/163570
 
dc.languageeng
 
dc.relation.ispartofProceedings of the 5th IASTED International Conference on Biomedical Engineering, BioMED 2007
 
dc.relation.referencesReferences in Scopus
 
dc.subjectBlood Glucose Variations
 
dc.subjectClassification Model
 
dc.subjectHeat Dissipation
 
dc.subjectResting Metabolic Rate
 
dc.titleA novel approach to estimate variations of blood glucose using noninvasive metabolic measurements
 
dc.typeConference_Paper
 
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Author Affiliations
  1. The University of Hong Kong