Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and ECG Signal
It addresses generalization challenges in hyperglycemia detection for medical applications, though it is incremental as it builds on existing ECG-based methods.
This paper tackles noninvasive hyperglycemia monitoring by integrating deep learning with ECG signals, achieving a detection performance of 91.60% AUC, 81.05% sensitivity, and 85.54% specificity on unseen subjects.
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.