Continuous Monitoring of Blood Pressure with Evidential Regression
This research addresses the need for continuous, non-invasive blood pressure monitoring for patients and healthcare providers, offering a method that meets established medical standards.
This paper introduces a method for continuous blood pressure monitoring using photoplethysmogram (PPG) signals, which meets healthcare criteria like AAMI and BHS standards. The method also provides uncertainty estimates for its predictions, and experiments on the MIMIC II database confirm its state-of-the-art performance and accurate uncertainty representation.
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.