重新评估基于光体积描记法（PPG）的无创血糖水平估计的可行性

中文摘要 AI 辅助

从光体积描记法（PPG）进行无创血糖水平（BGL）估计在可穿戴健康监测中具有巨大潜力，但由于数据集不一致、数据泄露和评估指标不标准化，不同研究的结果难以比较。我们提出了首个可复现、可扩展的评估流程，并使用该流程在已发布的数据集上，针对三种日益严格的数据划分协议（随机窗口级、参与者感知、部分参与者留一法（LSPO））重新评估五种有代表性的基于PPG的BGL方法。在随机划分下，模型表现出竞争力，但在参与者感知和LSPO评估下，模型性能崩溃，几乎所有模型都产生接近零或为负的R²值，与均值预测基准相当。关键的是，在所有模型和划分中，超过90%的预测结果落在临床可接受区域（克拉克误差网格A+B）内，包括基准模型。这揭示了一个根本性的脱节：临床区域指标系统性地掩盖了该领域模型的失效。我们的研究结果表明，由于样本级数据泄露，随机训练-测试划分会大幅高估基于PPG的BGL模型的泛化能力，且稳健的机器学习评估必须先于临床验证，才能有效评估其实际应用价值。

英文摘要

Non-invasive blood glucose level (BGL) estimation from photoplethysmography (PPG) holds great promise for wearable health monitoring, but results across studies are hard to compare due to inconsistent datasets, data leakage, and non-standardized evaluation metrics. We present the first reproducible, extensible evaluation pipeline and use it to reassess five representative PPG-based BGL methods on published datasets under three increasingly strict data-split protocols: random window-level, participant-aware, and leave-some-participants-out (LSPO). Models appeared competitive under random splitting but collapsed under participant-aware and LSPO evaluation, with nearly all yielding near-zero or negative R$^2$ values comparable to a mean-prediction baseline. Critically, across every model and split, over 90% of predictions fell within clinically acceptable zones (Clarke Error Grid A+B), including the baseline. This reveals a fundamental disconnect: clinical zone metrics systematically conceal model failure in this domain. Our findings demonstrate that random train-test splits substantially overestimate the generalization of PPG-based BGL models due to sample-level data leakage, and that robust ML evaluation must precede clinical validation to meaningfully assess real-world utility.

发表机构

• Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）

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