Continuous Monitoring of Blood Pressure with Evidential Regression

TL;DR

This paper introduces a novel PPG-based blood pressure monitoring method that provides continuous estimates along with uncertainty quantification, meeting healthcare standards and improving reliability over existing techniques.

Contribution

The proposed method advances blood pressure estimation by enabling continuous monitoring with uncertainty estimation, adhering to medical standards, and outperforming previous models.

Findings

Meets AAMI and BHS standards for BP measurement.

Provides reliable uncertainty estimates for predictions.

Achieves state-of-the-art performance on MIMIC II database.

Abstract

Photoplethysmogram (PPG) signal-based blood pressure (BP) estimation is a promising candidate for modern BP measurements, as PPG signals can be easily obtained from wearable devices in a non-invasive manner, allowing quick BP measurement. However, the performance of existing machine learning-based BP measuring methods still fall behind some BP measurement guidelines and most of them provide only point estimates of systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we present a cutting-edge method which is capable of continuously monitoring BP from the PPG signal and satisfies healthcare criteria such as the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) standards. Furthermore, the proposed method provides the reliability of the predicted BP by estimating its uncertainty to help diagnose medical condition based on the model prediction. Experiments on the MIMIC II database verify the state-of-the-art performance of the proposed method under several metrics and its ability to accurately represent uncertainty in prediction.