用皮肤电反应信号实现无创低血糖检测，成本低且适合可穿戴设备。
Toward Affordable and Non-Invasive Detection of Hypoglycemia: A Machine Learning Approach
- 仅用皮肤电反应数据，通过机器学习区分低血糖与正常血糖状态。
- LSTM模型实现100%低血糖召回率，置信区间内F1分数达0.611-0.745。
- 为资源有限地区提供低成本、非侵入式血糖监测新方案，适合穿戴设备部署。
糖尿病是全球日益严重的健康问题，尤其是1型糖尿病需持续监测以避免低血糖。尽管连续血糖监测（CGM）有效，但其高成本和侵入性限制了在资源匮乏地区的应用。本文提出一种基于皮肤电反应（GSR）的非侵入式方法，利用可穿戴传感器采集的生物信号进行血糖状态分类。研究使用合并的OhioT1DM 2018与2020数据集，构建机器学习流程，仅依靠GSR数据识别低血糖（葡萄糖<70 mg/dl）与正常血糖（葡萄糖>70 mg/dl）。共训练并评估七种模型：随机森林、XGBoost、MLP、CNN、LSTM、逻辑回归和K近邻。通过验证集和95%置信区间提升结果可靠性与鲁棒性。结果显示，LSTM模型在低血糖检测中达到完美召回率（1.00），F1分数置信区间为[0.611–0.745]；而XGBoost在类别不平衡下仍保持0.54的召回率。该方法展示了利用GSR数据开发低成本、可穿戴式血糖监测工具的潜力，适用于CGM难以普及的环境。
原文摘要 · Abstract (English)
Diabetes mellitus is a growing global health issue, with Type 1 Diabetes (T1D) requiring constant monitoring to avoid hypoglycemia. Although Continuous Glucose Monitors (CGMs) are effective, their cost and invasiveness limit access, particularly in low-resource settings. This paper proposes a non-invasive method to classify glycemic states using Galvanic Skin Response (GSR), a biosignal commonly captured by wearable sensors. We use the merged OhioT1DM 2018 and 2020 datasets to build a machine learning pipeline that detects hypoglycemia (glucose < 70 mg/dl) and normoglycemia (glucose > 70 mg/dl) with GSR alone. Seven models are trained and evaluated: Random Forest, XGBoost, MLP, CNN, LSTM, Logistic Regression, and K-Nearest Neighbors. Validation sets and 95% confidence intervals are reported to increase reliability and assess robustness. Results show that the LSTM model achieves a perfect hypoglycemia recall (1.00) with an F1-score confidence interval of [0.611-0.745], while XGBoost offers strong performance with a recall of 0.54 even under class imbalance. This approach highlights the potential for affordable, wearable-compatible glucose monitoring tools suitable for settings with limited CGM availability using GSR data. Index Terms: Hypoglycemia Detection, Galvanic Skin Response, Non Invasive Monitoring, Wearables, Machine Learning, Confidence Intervals.
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