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.

翻译：本文提出了一种基于心电图（ECG）的无创高血糖监测新方法，该方法利用包含1119名受试者的大规模数据库。先前利用心电图进行高血糖或血糖检测的研究受限于泛化能力和可扩展性方面的挑战，主要原因在于训练时使用了所有受试者的心电图数据，而未将未见过的受试者作为开发具有有效泛化能力方法的关键因素。我们设计了一种深度神经网络模型，能够识别不同空间位置上的显著特征，并分析每个卷积层内不同特征之间的相互依赖关系。为提升处理速度，我们将每位用户的心电图分割为单个心跳或单周期心电图。模型使用727名受试者的数据进行训练，168名受试者用于验证。测试阶段涉及224名未见过的受试者，数据集包含9000个分割片段。结果表明，所提算法能有效检测高血糖，曲线下面积（AUC）达91.60%，灵敏度为81.05%，特异度为85.54%。