This is a feasibility study in using radar for estimating blood pressure and which could allow for continuous, non-invasive measurements both inside and outside the clinic. Blood pressure has extensive use in emergency care for monitoring the state of a patient, in detection and treatment of hypertension as an important factor of cardiovascular diseases, in emerging applications and more. The invasive cannula for central, continuous, measurements and the sphygmomanometer for peripheral, punctual measurements are currently the reference tools for measuring blood pressure in the operating room and in the physician's office respectively. However, new blood pressure estimation devices could increase mobility in the hospital and reduce white-coat hypertension in the office. Moreover, such accessible and low-cost devices could extend measurements outside the clinic.The main work was focused on the estimation of the time-varying aortic radius as a prerequisite for the estimation of blood pressure. This work was conducted primarily based on theoretic considerations and simulations using realistic models of the human thorax. In addition, one article analyzed radar signatures from a phantom model and from an in vivo experiment to test findings. Radar echoes were analyzed for information regarding changes in the aorta radius. A magnetic resonance imaging study was conducted to map tissue movements as an aid to radar analysis.This paper studies the optimum signal choice for the estimation of the aortic blood pressure via aorta radius, using a monostatic radar configuration. The method involves developing the Cramér-Rao lower bound (CRLB) for a simplified model. The CRLB for model parameters are compared with simulation results using a grid-based approach for estimation. The CRLBs are within the 99% confidence intervals for all chosen parameter values. The CRLBs show an optimal region within an ellipsoid centered at 1 GHz center frequency and 1.25 GHz bandwidth with axes of 0.5 GHz and 1 GHz, respectively. Calculations show that emitted signal energy to received noise spectral density should exceed 10 12 for a precision of approximately 0.1 mm for a large range of model parameters. This implies a minimum average power of 0.4 μW. These values are based on optimistic assumptions. Reflections, improved propagation model, true receiver noise, and parameter ranges should be considered in a practical implementation.
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