著者

山越 康弘 小川 充洋 山越 健弘 田村 俊世 山越 憲一

出版者

社団法人日本生体医工学会

雑誌

生体医工学 : 日本エム・イー学会誌 (ISSN:1347443X)

巻号頁・発行日

vol.46, no.1, pp.49-57, 2008-02-10

参考文献数

27

被引用文献数

3

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金沢大学大学院自然科学研究科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.