Mueller Martin, Talary Mark S, Falco Lisa, De Feo Oscar, Stahel Werner A, Caduff Andreas
Research & Development Department, Solianis Monitoring AG, Zürich, Switzerland.
J Diabetes Sci Technol. 2011 May 1;5(3):694-702. doi: 10.1177/193229681100500324.
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.
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.
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.
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.
阻抗光谱法已被证明是一种用于人体无创连续血糖监测的候选方法。然而，除了葡萄糖外，其他因素也会对皮肤及皮下组织的阻抗特性产生影响。
通过主成分分析总结阻抗谱，并使用赤池信息准则识别相关变量。为了建立血糖模型，采用了线性最小二乘模型。应用蒙特卡罗模拟来检验个性化模型的效果。
主成分分析能够识别阻抗谱中的两种主要影响：与血糖相关的过程以及与皮肤和皮下组织保湿相关的平衡过程。使用全局线性最小二乘模型时，决定系数（R²）为0.60，而个性化模型的R²达到0.71。蒙特卡罗模拟证明了个性化模型相对于全局模型具有显著优势。
主成分分析有助于提取人体皮肤阻抗谱中与葡萄糖相关的影响。基于索利亚尼斯多传感器数据的线性全局模型对血糖估计具有良好的预测能力。然而，个性化线性模型仍具有更大的预测能力。
