Empirical comparison of deep ensembles and Monte Carlo dropout with GNLL and MSE losses, plus recalibration, shows DE and recalibrated GNLL perform best for predictive robustness and uncertainty calibration in PPG-based BP estimation under domain shift.
Deep learning for cuffless blood pressure estimation from photoplethysmography.IEEE Transactions on Biomedical Engineering, 66(7):1919–1930
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Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
Empirical comparison of deep ensembles and Monte Carlo dropout with GNLL and MSE losses, plus recalibration, shows DE and recalibrated GNLL perform best for predictive robustness and uncertainty calibration in PPG-based BP estimation under domain shift.