A Deep Sparse Capsule Network for Non-Invasive Blood Glucose Level Estimation Using a PPG Sensor

Authors

Narmatha Chellamani, University of Tabuk
Saleh Ali Albelwi, University of Tabuk
Manimurugan Shanmuganathan, University of Tabuk
Palanisamy Amirthalingam, University of Tabuk
Emad Muteb Alharbi, University of Tabuk
Hibah Qasem Salman Alatawi, University of Tabuk
Kousalya Prabahar, University of Tabuk
Jawhara Bader Aljabri, University of Tabuk
Anand Paul, LSU Health Sciences Center - New OrleansFollow

Document Type

Article

Publication Date

3-18-2025

Publication Title

Sensors

Abstract

Diabetes, a chronic medical condition, affects millions of people worldwide and requires consistent monitoring of blood glucose levels (BGLs). Traditional invasive methods for BGL monitoring can be challenging and painful for patients. This study introduces a non-invasive, deep learning (DL)-based approach to estimate BGL using photoplethysmography (PPG) signals. Specifically, a Deep Sparse Capsule Network (DSCNet) model is proposed to provide accurate and robust BGL monitoring. The proposed model’s workflow includes data collection, preprocessing, feature extraction, and predictions. A hardware module was designed using a PPG sensor and Raspberry Pi to collect patient data. In preprocessing, a Savitzky–Golay filter and moving average filter were applied to remove noise and preserve pulse form and high-frequency components. The DSCNet model was then applied to predict the sugar level. Two models were developed for prediction: a baseline model, DSCNet, and an enhanced model, DSCNet with self-attention. DSCNet’s performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Relative Difference (MARD), and coefficient of determination (R2), yielding values of 3.022, 0.05, 0.058, 0.062, 10.81, and 0.98, respectively.

PubMed ID

40293000

Volume

25

Issue

6

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Chellamani, Narmatha; Albelwi, Saleh Ali; Shanmuganathan, Manimurugan; Amirthalingam, Palanisamy; Alharbi, Emad Muteb; Alatawi, Hibah Qasem Salman; Prabahar, Kousalya; Aljabri, Jawhara Bader; and Paul, Anand, "A Deep Sparse Capsule Network for Non-Invasive Blood Glucose Level Estimation Using a PPG Sensor" (2025). School of Public Health Faculty Publications. 490.
https://digitalscholar.lsuhsc.edu/soph_facpubs/490
10.3390/s25061868