Early Warning Score (EWS) is a measure commonly used in hospitals since 90's to quantitatively assess the health of patients and predict its deterioration. Currently, nurses perform this assessment periodically by measuring respiration rate, oxygen saturation, systolic blood pressure, heart rate, core body temperature, and level of consciousness. Automation of this process using wearable devices allows for continuous monitoring inside and outside hospitals while reducing nurses' workload and monitoring costs. Current systems designed for this purpose use a separate device for measuring each of those bio-metric signals. This presents a challenge for the comfort and practicality of use in a real-life setup and increases its associated costs. In this work, we present a new method for estimation of systolic blood pressure, which allows reduction of the number of sensors. In our proposed method we use a smartwatch Photoplethysmogram (PPG) signal, which is mainly used for hear rate estimation, to estimate the (systolic) blood pressure too. An important feature of this system, in contrast to State-of-the-Art (SoA), is continuous, easy, and comfortable monitoring of blood pressure.
Authors: Nima TaheriNejad (Institute of Computer Technology, TU Wien, Austria), Yasaman Rahmati (TU Wien),
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