http://swrc.ontoware.org/ontology#Article
近赤外瞬時差分分光法による非観血的血糖計測のための多変量校正モデルの検討
ja
pulse glucometry
non-invasive blood glucose measurement
multivariate analysis
support vector machines
nonlinear problem
山越 康弘
小川 充洋
山越 健弘
田村 俊世
山越 憲一
金沢大学大学院自然科学研究科
生体医工学 : 日本エム・イー学会誌
46
1
49-57
2008-02-10
1347-443X
AA11633569
日本生体医工学会
An optical method recently proposed for non-invasive in vivo blood glucose concentration (BGL) measurement, named "Pulse Glucometry", was combined and compared with four multivariate analyses for constructing calibration models: Principal Component Regression (PCR), Partial Least Squares Regression (PLS), Artificial Neural Network (ANN), Support Vector Machines Regression (SVMsR). A very fast spectrophotometer for "Pulse Glucometry" provides the total transmitted radiation spectrum (I_λ) and the cardiac-related pulsatile component (ΔI_λ) superimposed on I_λ in human fingertips over a wavelength range from 900 to 1700 nm with resolution of 8 nm in 100 Hz sampling. From a family of I_λs measured, which include information relating to blood constituent such as BGL values, differential optical densities (ΔOD_λs, where ΔOD_λ=Log(1+ΔI_λ/I_λ)) were obtained and normalized by the ΔOD_λ values at 1100 nm. Finally, the 2nd derivatives of the normalized ΔOD_λs(Δ^2OD_λs) along wavelengths were calculated as regressors. Subsequently, calibration models from paired data sets of regressors(the values of Δ^2OD_λs) and regressand (the corresponding known BGL values) were constructed with PCR, PLS, ANN and SVMsR. The results show that each calibration model provides a relatively good regression with a modified 5-fold cross validation for total 95 paired data, in which the BGLs ranged from 100.7-246.3 mg/dl. The results were evaluated by the Clarke error grid analysis and all data points obtained from all calibration models fell within the clinically acceptable regions (region A or B). Among them, ANN and SVMsR calibration provided the best plot distributions (in ANN; Region A: 77 plots (81.1%), B: 18 plots (18.9%). in SVMsR; Region A: 78 (82.1%), B: 17 (17.9%)). Total calculation time of SVMsR is about 100 times shorter than ANN. These results suggest that a calibration model using SVMsR is highly promising for "Pulse Glucometry.
http://ci.nii.ac.jp/naid/110006649735/