# AI-PPG-ACC-HTN

Google Research, with Beth Israel Deaconess Medical Center and Harvard Medical School, used the photoplethysmography and accelerometer signals of a consumer smartwatch to passively identify a persistent hypertensive state without cuff calibration. Rather than estimating systolic and diastolic pressure at a moment in time, the system averages 15-second signal features over multiple days and combines them with height in a regularized logistic regression that outputs a probability. Against 24-hour ambulatory blood pressure monitoring in 196 participants free of known cardiovascular disease and not taking blood pressure medication, initial screening reached 65.8% sensitivity and 90.0% specificity, versus 55.3% sensitivity for concurrent office screening.

Google Research, with Beth Israel Deaconess Medical Center and Harvard Medical School, used the photoplethysmography and accelerometer signals of a consumer smartwatch to passively identify a persistent hypertensive state without cuff calibration. Rather than estimating systolic and diastolic pressure at a moment in time, the system averages 15-second signal features over multiple days and combines them with height in a regularized logistic regression that outputs a probability. Against 24-hour ambulatory blood pressure monitoring in 196 participants free of known cardiovascular disease and not taking blood pressure medication, initial screening reached 65.8% sensitivity and 90.0% specificity, versus 55.3% sensitivity for concurrent office screening.