Accurately detecting hypoglycemia without invasive glucose sensors remains a critical challenge in diabetes management, particularly in regions where continuous glucose monitoring (CGM) is prohibitively expensive or clinically inaccessible. This extended study introduces a comprehensive, multimodal physiological framework for non-invasive hypoglycemia detection using wearable sensor signals. Unlike prior work limited to single-signal analysis, this chapter evaluates three physiological modalities, galvanic skin response (GSR), heart rate (HR), and their combined fusion, using the OhioT1DM 2018 dataset. We develop an end-to-end pipeline that integrates advanced preprocessing, temporal windowing, handcrafted and sequence-based feature extraction, early and late fusion strategies, and a broad spectrum of machine learning and deep temporal models, including CNNs, LSTMs, GRUs, and TCNs. Our results demonstrate that physiological signals exhibit distinct autonomic patterns preceding hypoglycemia and that combining GSR with HR consistently enhances detection sensitivity and stability compared to single-signal models. Multimodal deep learning architectures achieve the most reliable performance, particularly in recall, the most clinically urgent metric. Ablation studies further highlight the complementary contributions of each modality, strengthening the case for affordable, sensor-based glycemic monitoring. The findings show that real-time hypoglycemia detection is achievable using only inexpensive, non-invasive wearable sensors, offering a pathway toward accessible glucose monitoring in underserved communities and low-resource healthcare environments.

翻译：准确检测低血糖而不依赖侵入式葡萄糖传感器，仍然是糖尿病管理中的一个关键挑战，在连续血糖监测（CGM）成本过高或临床无法获取的地区尤其如此。这项扩展研究引入了一个全面的、多模态生理学框架，用于利用可穿戴传感器信号进行非侵入式低血糖检测。与以往局限于单一信号分析的工作不同，本章利用OhioT1DM 2018数据集，评估了三种生理模态：皮肤电反应（GSR）、心率（HR）以及它们的融合信号。我们开发了一个端到端的处理流程，集成了先进的预处理、时间窗划分、手工与基于序列的特征提取、早期与晚期融合策略，以及广泛的机器学习和深度时序模型，包括CNN、LSTM、GRU和TCN。我们的结果表明，生理信号在低血糖发生前表现出明显的自主神经模式，并且与单信号模型相比，结合GSR与HR能持续提升检测的敏感性和稳定性。多模态深度学习架构实现了最可靠的性能，尤其是在临床最紧迫的指标——召回率上。消融研究进一步凸显了每种模态的互补贡献，强化了基于传感器的、经济可负担的血糖监测方案的可行性。研究结果表明，仅使用廉价、非侵入式的可穿戴传感器即可实现实时低血糖检测，为在医疗服务不足社区和资源有限的医疗环境中实现可及的血糖监测提供了一条途径。