title: Research on the multiple linear regression in non-invasive blood glucose measurement.
abstract: A non-invasive blood glucose measurement sensor and the data process algorithm based on the metabolic energy conservation (MEC) method are presented in this paper. The physiological parameters of human fingertip can be measured by various sensing modalities, and blood glucose value can be evaluated with the physiological parameters by the multiple linear regression analysis. Five methods such as enter, remove, forward, backward and stepwise in multiple linear regression were compared, and the backward method had the best performance. The best correlation coefficient was 0.876 with the standard error of the estimate 0.534, and the significance was 0.012 (sig. <0.05), which indicated the regression equation was valid. The Clarke error grid analysis was performed to compare the MEC method with the hexokinase method, using 200 data points. The correlation coefficient R was 0.867 and all of the points were located in Zone A and Zone B, which shows the MEC method provides a feasible and valid way for non-invasive blood glucose measurement.
authors.affiliation: School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China.|Guangxi Experiment Center of Information Science, Guilin 541004, China.
authors.forename: Jianming
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Guangxi Experiment Center of Information Science, Guilin 541004, China.
authors.forename: Zhencheng
authors.initials: Z
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis* / Q000378:metabolism; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation* / Q000379:methods; D001831:Body Temperature / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005385:Fingers / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D017156:Photoplethysmography / Q000295:instrumentation*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose
keywords: Clarke error gird; Non-invasive blood glucose measurement; metabolic energy conservation; multiple linear regression; sensor