A Triplet-Loss Embedded Deep Regressor Network for Estimating Blood Pressure Changes Using Prosodic Features

Abstract

Studies have shown that measures of personal physiology, e.g., blood pressure (BP) variation and heart rate variability (HRV), is closely related to a subject's psychological states and are being used regularly to track patients' health conditions in medical settings. The conventional method of monitoring physiology requires wearing specialized sensors or utilizing medical instruments, which hinders the ability of scalable and just-in-time monitoring of patients. In this study, we propose a triplet-loss embedded deep regressor network to predict changes of BP using expressive prosodic features for on-boarding emergency room patients between pre- and post-triage sessions. The framework achieves correlations of 0.419 and 0.386 in predicting changes in SBP (systolic blood pressure) and DBP (diastolic blood pressure) respectively, which is 26.1% and 17.3% relative improvement compared to DNN-regressors without triplet-loss embedding. Further correlation analyses on the relationship between prosodic features and BP changes are presented.