Advancements in Continuous Glucose Monitoring: Integrating Deep Learning
  and ECG Signal
MohammadReza Hosseinzadehketilateh Banafsheh Adami Nima Karimian
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.