Data processing for noninvasive continuous glucose monitoring with a multisensor device.
- Mueller M Research & Development Department, Solianis Monitoring AG, Zürich, Switzerland. andreas.caduff@solianis.com
- Talary MS
- Falco L
- De Feo O
- Stahel WA
- Caduff A
- 2011-07-05
Published in:
- Journal of diabetes science and technology. - 2011
Adult
Blood Glucose
Blood Glucose Self-Monitoring
Dielectric Spectroscopy
Electric Impedance
Equipment Design
Female
Humans
Least-Squares Analysis
Linear Models
Male
Materials Testing
Middle Aged
Monte Carlo Method
Perfusion
Predictive Value of Tests
Principal Component Analysis
Skin
Time Factors
English
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 (R²) of 0.60 was achieved, whereas the personalized model reached an R² 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.
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 (R²) of 0.60 was achieved, whereas the personalized model reached an R² 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.
- Language
-
- English
- Open access status
- bronze
- Identifiers
-
- DOI 10.1177/193229681100500324
- PMID 21722585
- Persistent URL
- https://sonar.ch/global/documents/113006
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