Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
Not on the map. Why?
Abstract (arXiv)
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
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 49 references that are in the map. First posted on arXiv 18 May 2026.
Builds on
20 of 49 references.
References · 20
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2017On Calibration of Modern Neural Networkscited by 4,960
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
- 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
- 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
- 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
- 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 496
- 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 422
- 2020fastai: A Layered API for Deep Learningcited by 183
- 2020Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLcited by 158
8 more
- 2019Evaluating and Calibrating Uncertainty Prediction in Regression Taskscited by 87
- 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 63
- 2025Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Studycited by 9
- 2025Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approachescited by 8
- 2023How to Use Dropout Correctly on Residual Networks with Batch Normalizationcited by 7
- 2025Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction taskscited by 3
- 2025A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysiscited by 1
- 2026Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPGcited by 1
Built on by
Not cited yet.