RESEARCH

HOME RESEARCH
Health Analytics
Other: Computation Methods for Health
Speech and Language
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 posttriage 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 DNNregressors without triplet-loss embedding. Further correlation analyses on the relationship between prosodic features and BP changes are presented.
Figures
Our proposed fisher-vector based deep triplet-loss embedded deep regressor network to predict changes in blood pressure between pre- and post- intervention at emergency triage using prosodic features.
Our proposed fisher-vector based deep triplet-loss embedded deep regressor network to predict changes in blood pressure between pre- and post- intervention at emergency triage using prosodic features.
Keywords
behavioral signal processing | triplet loss embedding | blood pressure | prosody | triage session
Authors
Publication Date
2018/04/15
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI
10.1109/icassp.2018.8461933
Publisher
IEEE