研究心率监测中模型在数据分布偏移下的可靠性，提升无袖带血压预测可信度。
Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
- 用深度集成增强模型在外部数据上的鲁棒性，优于蒙特卡洛丢弃。
- 基于GNLL的不确定性校准效果最佳，尤其配合共形预测或温度缩放。
- 建议在真实医疗场景中同时评估准确率与不确定性校准，确保安全可靠。
不确定性量化（UQ）对医疗等高风险领域至关重要，但极少在真实分布外（OOD）条件下评估。本文评估了基于脉搏波（PPG）信号的深度学习血压（BP）估计在分布内（ID）和分布外（OOD）条件下的预测性能与不确定性可靠性。使用在PulseDB上训练的XResNet1D-50，在四个外部数据集上测试，对比深度集成（DE）与蒙特卡洛丢弃（MCD），结合高斯负对数似然（GNLL）与均方误差（MSE）损失，并通过共形预测（CP）、温度缩放（TS）、等熵回归（IR）进行后处理校准。主要发现：（1）在外部分布偏移下，DE比MCD更具预测鲁棒性；（2）经校准的GNLL方法表现最优（如SBP：GNLL+DE+CP，DBP：GNLL+DE+TS），而MSE-based不确定性需校准才具实用性；（3）CP与TS带来最一致的提升，IR在部分情形下仍具竞争力。结论表明，DE方法在分布偏移下最稳健，GNLL最适合原生不确定性量化，校准是使MSE-based不确定性实用的关键。研究强调需在外部数据上联合评估准确性与校准性，以实现可信的无袖带血压估计。
原文摘要 · Abstract (English)
Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty reliability for deep learning-based blood pressure (BP) estimation from photoplethysmography (PPG) signals under both in-distribution (ID) and OOD settings. Using an XResNet1D-50 trained on PulseDB and tested on four external datasets, we compared deep ensembles (DE) and Monte Carlo dropout (MCD) with Gaussian negative log-likelihood (GNLL) and mean squared error (MSE) losses, optionally followed by post-hoc recalibration via conformal prediction (CP), temperature scaling (TS), and isotonic regression (IR). The key findings of our study are as follows: (1) DE provides stronger predictive robustness under domain shift than MCD, an advantage that becomes clear primarily under external shift. (2) Recalibrated GNLL-based methods yield the best uncertainty calibration (e.g., GNLL+DE+CP for systolic blood pressure (SBP), GNLL+DE+TS for diastolic blood pressure (DBP)), while MSE-based uncertainty requires recalibration to become practically useful. (3) Across settings, CP and TS offer the most consistent gains, with IR remaining competitive in several cases. Overall, our results identify DE-based methods as most robust for predictive performance under domain shift, GNLL as strongest for native UQ, and recalibration as essential for making MSE-based uncertainty practical. These findings highlight the need to jointly assess predictive accuracy and calibration on external data for trustworthy cuffless BP estimation
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