Uncertainty Quantification of Cuffless Blood Pressure with Deep Evidential Regression Model

Cuffless blood pressure (BP) estimation models have been extensively studied in recent years. However, due to aleatoric and epistemic uncertainty, these methods make it difficult to provide reliable and accurate BP estimations meeting the clinical requirement. In this study, we propose a novel method to quantify the uncertainty of the cuffless BP estimation model and combine epistemic uncertainty with conformal prediction to generate a statistically rigorous uncertainty interval (UI). First, we develop a deep evidential regression (DER) model to estimate BP with five features extracted from a noninvasive photoplethysmogram (PPG) signal. The DER model predicts a distribution of the target BP instead of a point estimation, so it can offer an analytical solution for the estimation uncertainty. We then utilize conformal prediction to generate a UI that covers the reference BP with a defined probability. We validate the proposed method on 37 subjects with continuous Finpres BP as a reference. The results show that the mean absolute difference (MAD) of systolic BP (SBP) and diastolic BP (DBP) estimations with the proposed method are 5.56 and 3.18 mmHg, respectively. The estimated UI can capture the reference SBP and DBP with a coverage rate of 94.8% and 95.9%, respectively. The findings indicate that the proposed method has the potential to empower more reliable and accurate cuffless BP measurement.