GlucoNet: A Hybrid Learning Methodology for Non-Invasive Blood Glucose Estimation from PPG

The growing prevalence of the diabetic population across the world makes it absolutely necessary to monitor the blood glucose level (BGL) on a regular basis. Although, conventional BGL monitoring methods are widely used for clinical intervention as well as individual monitoring of BGL, those are invasive, painful, and unsuitable for continuous monitoring. These limitations lead to the development of noninvasive alternative methodologies, such as photoplethysmography(PPG) based glucose level estimation. PPG has emerged as a promising method for monitoring BGL, offering several advantages over other optical techniques. In this work, a deep learning architecture, GlucoNet has been introduced that combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules for learning both spatial and temporal features from PPG to estimate blood glucose levels from PPG signals. Our model was trained and validated on the publicly available online dataset and real-world dataset to estimate its robustness. Our proposed model outperforms the state of the art methods by achieving a mean absolute error (MAE) of 2.15 mg/dL, root mean squared error (RMSE) of 3.28 mg/dL, and an R² of 0.99. Furthermore, our model demonstrates exceptional clinical performance, with 100% of predictions falling within the clinically acceptable zones A and B of the Clarke Error Grid analysis.Clinical relevance-This study introduces a non-invasive, real-time methodology for blood glucose level estimation using only PPG signal. This model ensures painless, and continuous monitoring of BGL, and can be used as a reliable solution for early detection and proper management of diabetes and related complications.