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In medical field, many cardiovascular and correlated diseases can be early treated by monitoring and analyzing the subject’s blood pressure (BP). However, the measurement of blood pressure requires the use of invasive medical and health equipment, including the classical sphygmomanometer or the digital pressure meter. In this paper, we proposed an innovative algorithmic pipeline to properly estimate the systolic and diastolic blood pressure of a subject through the visio-reconstruction of the PhotoPlethysmoGraphic (PPG) signal. By means of an innovative method of face-motion magnification through Deep Learning, it is possible to visioreconstruct specific points of the PPG signal in order to extract features related to the pressure level of the analyzed subject. The proposed approach can be used effectively in healthcare facilities for the fast and noninvasive monitoring of the pressure level of subjects or in other similar applications. We compared our results using a classic cuff-less blood pressure device with encouraging results that reach 92% in accuracy.

Deep Visio-PhotoPlethysmoGraphy Reconstruction Pipeline for Non-invasive Cuff-less Blood Pressure Estimation