Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals | JoVE Visualize

Area of Science:

Biomedical Engineering
Machine Learning
Data Science

Background:

Diabetes management requires accurate blood glucose level (BGL) monitoring to prevent severe complications.
Traditional BGL monitoring methods (e.g., finger-prick tests) suffer from low patient adherence due to invasiveness.
Non-invasive BGL monitoring techniques are crucial for improving patient compliance and long-term health outcomes.

Purpose of the Study:

To develop a privacy-preserving, non-invasive method for blood glucose level (BGL) monitoring using photoplethysmography (PPG) signals.
To leverage federated learning (FL) for collaborative training of deep neural networks (DNNs) across multiple institutions without data sharing.
To enhance the accuracy and clinical reliability of non-invasive BGL prediction through advanced signal processing and feature selection.

Main Methods:

Photoplethysmography (PPG) signals were preprocessed using continuous wavelet transform (CWT) for noise reduction and adaptive cycle-based segmentation (ACBS).
Particle swarm optimization (PSO) was employed for feature selection to enhance classification accuracy.
A federated learning (FL) framework enabled collaborative training of deep neural networks (DNNs) on distributed datasets from multiple healthcare organizations.

Main Results:

The proposed FL-based system achieved a root mean square error (RMSE) of 19.1 mg/dL on diverse datasets (VitalDB, MUST).
Clarke error grid analysis (CEGA) demonstrated high clinical reliability, with 99.31% of predictions within acceptable ranges.
The federated learning approach significantly outperformed conventional centralized deep learning models in predictive accuracy.

Conclusions:

Federated learning offers a robust solution for privacy-preserving, collaborative training of non-invasive glucose monitoring models.
The integration of CWT, ACBS, and PSO with FL enhances the accuracy and clinical utility of PPG-based BGL prediction.
This approach represents a significant advancement towards widespread adoption of non-invasive glucose monitoring for diabetes management.