الفهرس 
C H A P T E R 1Only 14 pages are availabe for public view
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Abstract
A new tool for the estimation of both the central arterial pressure and the unknown channel dynamics has been developed. Given two peripheral arterial pressure waveform measurements this new signal processing algorithm generates two models that represent the distinct branch dynamic behavior associated with the measured signals. The framework for this methodology is based on a multi-channel blind deconvolution (MBD) technique that has been reformulated to use stochastic calculus (SC). The technique is based on (MBD) of dynamic system, in which two or more measured outputs (peripheral artery pressure waveforms from the femoral and radial arteries) of a single input (central arterial pressure CAP) are mathematically analyzed, in order to reconstruct the common unobserved CAP within an arbitrary scale factor.
The convolution process is modeled as a Finite Impulse Response (FIR) filter with unknown coefficients. The source signal is also unknown. Assuming that some or all of the FIR filter coefficients are time-varying, we have been able to get accurate estimation results for the source signal, even though the filter order is unknown. The time-varying filter coefficients have been estimated through the (SC) algorithm, and we have been able to deconvolve the measurements and obtain both the source signal and the convolution path or filter. The positive results demonstrate that the SC approach is superior to conventional methods.
This thesis develops a central hemodynamic monitoring based on SC. Analysis of the data from a cardiovascular simulator and animal experiments verify the validity of this scheme. The positive results demonstrate that the SC approach could open up the possibility for noninvasive central hemodynamic monitoring, which could significantly reduce the risks to which patients are exposed.