Data Processing for Noninvasive Continuous Glucose Monitoring with a Multisensor Device
| Autor: | Andreas Caduff, Mark S. Talary, Martin Mueller, Oscar De Feo, Werner A. Stahel, Lisa Falco |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Adult
Blood Glucose Male Time Factors Computer science Endocrinology Diabetes and Metabolism Monte Carlo method Biomedical Engineering Bioengineering Feature selection Predictive Value of Tests Blood Glucose Self-Monitoring Materials Testing Linear regression Statistics Electric Impedance Internal Medicine Humans Least-Squares Analysis Skin Principal Component Analysis Linear model Functional data analysis Equipment Design Middle Aged Perfusion Dielectric Spectroscopy Principal component analysis Linear Models Original Article Female Akaike information criterion Biological system Monte Carlo Method |
| Zdroj: | Journal of Diabetes Science and Technology. 5:694-702 |
| ISSN: | 1932-2968 |
| DOI: | 10.1177/193229681100500324 |
| Popis: | Background: Impedance spectroscopy has been shown to be a candidate for noninvasive continuous glucose monitoring in humans. However, in addition to glucose, other factors also have effects on impedance characteristics of the skin and underlying tissue. Method: Impedance spectra were summarized through a principal component analysis and relevant variables were identified with Akaike’s information criterion. In order to model blood glucose, a linear least-squares model was used. A Monte Carlo simulation was applied to examine the effects of personalizing models. Results: The principal component analysis was able to identify two major effects in the impedance spectra: a blood glucose-related process and an equilibration process related to moisturization of the skin and underlying tissue. With a global linear least-squares model, a coefficient of determination ( R 2 ) of 0.60 was achieved, whereas the personalized model reached an R 2 of 0.71. The Monte Carlo simulation proved a significant advantage of personalized models over global models. Conclusion: A principal component analysis is useful for extracting glucose-related effects in the impedance spectra of human skin. A linear global model based on Solianis Multisensor data yields a good predictive power for blood glucose estimation. However, a personalized linear model still has greater predictive power. |
| Databáze: | OpenAIRE |
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