Deep learning analysis for blood glucose monitoring using near infrared spectroscopy

Background and Objective: The race for the next generation of painless and reliable glucose monitoring for diabetes is on. Near infrared spectroscopy has become a promising technology among others for blood glucose monitoring. While advances have been made, the reliability and the calibration of non-invasive instruments could still be improved. The objective of this study was to set up a non-invasive blood glucose measurement device based on deep learning analysis to detect the spectral response from human tissue.
Methods: This study successfully adopted the four-stage framework of bio-signal processing to handle the near infrared spectroscopy that is used to measure blood glucose level. The major contributions the study makes include the selection of pre-processing methods (generalized least squares weighting pre-filter) and the algorithm for feature selection (genetic algorithm) and developing computational algorithm (partial least squares discriminant analysis using Monte Carlo) to improve the performance and accuracy rate of the calculation.
Results: An improved method based on Monte Carlo approach for the partial least squares is proposed. The overall classification rate of the model reached 75.2%. This algorithm outperforms conventional multivariate methods, whereby predicting the relationship between the response and the independent variables is more accurate, thus enhancing the reliability of the regression model.
Conclusion: The findings obtained in the study provide a useful reference for future development in non-invasive blood glucose measurement. Copyright © 2019 CF So.