Improving non-invasive glucose estimation with monthly calibrated photoplethysmography and implicit HbA1c
Year: 2025
Type: article
Abstract: Most noninvasive blood glucose technologies, especially wearable photoplethysmography devices, require multiple calibrations and are often limited to narrow cohorts such as unmedicated or mild cases. We assess whether a single pretest once per month can meet clinical accuracy while broadening applicability through cohort-specific models. We develop models for three groups: (i) individuals not using antidiabetic drugs, (ii) those using oral antidiabetic drugs only, and (iii) those using antidiabetic drugs in combination with other medications. Models are trained on cohort data with and without the monthly pretest and are then applied directly to personal testing without retraining. Inputs include dual-channel photoplethysmography signals and an inferred HbA1c (glycated hemoglobin) feature. Accuracy is summarized by mean absolute relative difference, clinical safety by the Parkes Error Grid, and improvements by a nonparametric rank-sum test. Here, we show that the best models using a single monthly pretest achieve mean absolute relative differences of 9.59, 12.23, and 16.40% for groups (i), (ii), and (iii), respectively. In the most complex group (iii), prediction errors are significantly lower than our earlier work according to the rank-sum test. The single-pretest models produce no clinically unacceptable readings on the Parkes Error Grid, likely due to dual-channel input and the inferred glycated-hemoglobin feature. A single monthly pretest enables accurate and clinically safe noninvasive glucose measurement across diverse patient groups. The approach operates without retraining or fine-tuning and can adapt to new users and devices through edge computing, supporting integration into current wearables for everyday diabetes management and public-health prevention. People with diabetes are unable to control the amount of sugar in their blood, with levels often being too high. They often need to prick their fingers regularly to check blood sugar levels. We investigated whether a light-based wearable device, combined with a brief calibration test once a month, could accurately estimate blood sugar levels when people had not recently eaten on a daily basis. The study evaluated how well the device worked for people who did not use diabetes drugs, people who used diabetes pills and people who used diabetes pills with other medicines. Accurate readings were seen for up to 30 days after the calibration and produced no clinically unsafe results. This approach could reduce the need for finger-prick tests and help more people track and manage their diabetes at home. Chu et al. develop a monthly-calibrated photoplethysmography pipeline that leverages an implicit HbA1c with PPG signals to non-invasively estimate fasting blood glucose across three diabetic cohorts. Improved accuracy is seen over prior approaches, as assessed by mean absolute relative differences and Parkes Error Grid analyses.
Source: Communications Medicine
Authors: Justin Chu, Tung-Han Hsieh, Yao-Ting Chang, Shien-Kuei Liaw, Fu-Liang Yang
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,690
OpenAlex ID: W7117124052