Multiparametric cloth-based wearable, SimpleSense, estimates blood pressure

Abstract

AbstractTargeted maintenance of blood pressure for hypertensive patients requires accurate monitoring of blood pressure at home. Use of multiparametric vital signs ECG, heart sounds, and thoracic impedance for blood pressure estimation at home has not been reported previously. In an observational multi-site study, 120 subjects (female (N = 61, 52%)) between 18 and 83 years of age were recruited with the following stratification (Normal (20%), prehypertensive (37%), stage 1(26%), and stage 2 (18%). From these subjects, 1686 measurements of blood pressure from a sphygmomanometer were associated with simultaneously acquired signals from the SimpleSense device. An ensemble of tree-based models was trained with inputs as metrics derived from the multiparametric and patient demographics data. A test Mean Absolute Difference (MAD) of ± 6.38 mm of Hg and ± 5.10 mm of Hg were obtained for systolic and diastolic blood pressures (SBP; DBP), respectively. Comparatively, the MAD for wrist-worn blood pressure cuff OMRON BP6350 (GUDID—10073796266353) was ± 8.92 mm of Hg and ± 6.86 mm of Hg, respectively. Machine learning models trained to use multiparametric data can monitor SBP and DBP without the need for calibration, and with accuracy levels comparable to at-home cuff-based blood pressure monitors.