Fingertip capillary dynamic near infrared spectrum (DNIRS) measurement combined with multivariate linear modification algorithm for noninvasive blood glucose monitoring

Blood glucose continuous monitoring (GCM) plays a crucial role in prevention and diagnosis of diabetes. A noninvasive CGM method based on fingertip capillary Dynamic Near Infrared Spectrum (DNIRS) combined with multivariate linear modification algorithm was proposed in this study. In order to control the environmental variables and physicochemical parameters during spectral data acquisition, a Fingertip Fixed Probe Biosensor (FFPB) was designed. In the preliminary experiment, three group of volunteers (healthy young people, middleaged people and patients with diabetes) were test twice. The model established by the former test could be used for the latter prediction for each individual, and the duration of each test was 120 min. Meanwhile the reference value of blood glucose was measured by the standard blood glucose analyzer. When establishing the prediction model, a multivariate linear modification algorithm was proposed, which has better prediction accuracy and precision than the traditional multiple linear regression model. The root mean square error of validation and root mean square error of prediction are RMSEV < 15.61 mg/dL and RMSEP < 20.67 mg/dL respectively. The correlation coefficient between the prediction and the reference value of blood glucose also reaches 0.87, and the prediction keeps good track of postprandial glucose excursions with the comparison of the reference value. Through the Clark Error Grid Analysis (CEGA), more than 96 % of the test set samples lies within Zone A. The result indicates that the prediction model has good prediction accuracy and robustness. This measurement method can continuously and noninvasively monitor the variance of blood glucose in human body, which has a promising prospect in the future practical application.

Automated summary

The study proposed a noninvasive CGM method based on DNIRS, utilizing FFPB to control environmental variables and physicochemical parameters during spectral data acquisition. The results show that the model has good prediction accuracy and robustness, allowing for continuous monitoring of blood glucose variations in the human body.