Uncertainty quantification of cuffless blood pressure estimation based on parameterized model evidential ensemble learning

Cuffless blood pressure (BP) measurement has the potential to break through the way to detect and prevent hypertension, but it is still challenging in meeting clinical performance requirements. The limited accuracy of current cuffless BP measurement is mainly attributed to the epistemic (model) and aleatoric (data) uncertainties of the estimation methods. However, few previous studies have considered this problem. In this study, we propose a parameterized model evidential ensemble learning (PEEL) framework with the aim to reduce the model uncertainty (so as to improve the performance) and quantify the uncertainty. The PEEL framework consists of two stages: original BP estimations with parameterized estimation models in the first stage, and, a neural network to estimate the evidential distribution of the final BP estimation in the second stage. Experiments on 96 subjects with MIMIC III dataset show that the estimation error with the PEEL model for systolic and diastolic BP is 3.74 mmHg and 2.22 mmHg, respectively. PEEL model has the potential to reduce the model uncertainty and to improve the performance of cuffless BP estimation. Furthermore, the estimated uncertainty can be used as a confidence interval to assist in diagnosing hypertension and support clinical decisions.