Cuffless Blood Pressure Monitoring from an Array of Wrist Bio-Impedance Sensors Using Subject-Specific Regression Models: Proof of Concept.

Abstract

Continuous and beat-to-beat monitoring of blood pressure (BP), compared to office-based BP measurement, provides significant advantages in predicting future cardiovascular disease. Traditional BP measurement methods are based on a cuff, which is bulky, obtrusive and not applicable to continuous monitoring. Measurement of pulse transit time (PTT) is one of the prominent cuffless methods for continuous BP monitoring. PTT is the time taken by the pressure pulse to travel between two points in an arterial vessel, which is correlated with the BP. In this paper, we present a new cuffless BP method using an array of wrist-worn bio-impedance sensors placed on the radial and the ulnar arteries of the wrist to monitor the arterial pressure pulse from the blood volume changes at each sensor site. BP is accurately estimated by using AdaBoost regression model based on selected arterial pressure pulse features such as transit time, amplitude and slope of the pressure pulse, which are dependent on the cardiac activity and the vascular properties of the wrist arteries. A separate model is developed for each subject based on calibration data to capture the individual variations of BP parameters. In this pilot study, data was collected from 10 healthy participants with age ranges from 18 to 30 years after exercising using our custom low-noise bio-impedance sensing hardware. Post-exercise BP was accurately estimated with an average correlation coefficient and root mean square error (RMSE) of 0.77 and 2.6 mmHg for the diastolic BP and 0.86 and 3.4 mmHg for the systolic BP.