Noncontact Blood Pressure Estimation Using BP-Related Cardiovascular Knowledge: An Uncalibrated Method Based on Consumer-Level Camera

Abstract

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> : The tiny change of skin color, caused by heartbeat, can be captured with consumer-level cameras by using the imaging photoplethysmography (iPPG) technique, offering a non-contact way of extracting pulse signals. The pulse signals have been demonstrated to contain information of human physiological characteristics and have been used for blood pressure (BP) estimation in recent years. According to BP-related cardiovascular knowledge, this paper presents a new method for BP estimation based on the iPPG pulse signals, featured by incorporating cardiovascular characteristics including heart rate (HR), stroke volume (SV), elasticity of vessel walls (EVW), and peripheral vascular resistance (PVR). Correlations between the systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and the cardiovascular characteristics are extracted, which facilitates the selection of pulse features consistent with BP properties. Based on the selected features, two Bayesian neural network (BNN) models are constructed for the estimation of SBP and DBP respectively, where the machine learning (ML) uncertainty of the estimation is also evaluated. This method is uncalibrated that means it can work without additional information except for the videos from the camera. The proposed method has been tested on 220 patients with a history of cardiovascular diseases. Errors of the BP estimation are 9±13 (MAE±STD) mmHg for SBP, 7±10 (MAE±STD) mmHg for DBP, and the ML uncertainty of the estimation indicates reliability of the proposed method.