# Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and ECG Signal

arXiv: https://arxiv.org/abs/2403.07296 · HTML: https://mlsanity.com/paper/2403.07296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach](https://mlsanity.com/paper/2311.04148/) (2023)

## Abstract (arXiv)

This paper presents a novel approach to noninvasive hyperglycemia monitoring utilizing electrocardiograms (ECG) from an extensive database comprising 1119 subjects. Previous research on hyperglycemia or glucose detection using ECG has been constrained by challenges related to generalization and scalability, primarily due to using all subjects' ECG in training without considering unseen subjects as a critical factor for developing methods with effective generalization. We designed a deep neural network model capable of identifying significant features across various spatial locations and examining the interdependencies among different features within each convolutional layer. To expedite processing speed, we segment the ECG of each user to isolate one heartbeat or one cycle of the ECG. Our model was trained using data from 727 subjects, while 168 were used for validation. The testing phase involved 224 unseen subjects, with a dataset consisting of 9,000 segments. The result indicates that the proposed algorithm effectively detects hyperglycemia with a 91.60% area under the curve (AUC), 81.05% sensitivity, and 85.54% specificity.