New Wiener System based modeling and signal processing method for characterization of vascular function
Creators: name: Patel, Amit
Publisher: name: No Publisher Supplied
Year: 2017

description: Central aortic blood pressure waveform (P_a) is a critical determinant of the state of overall cardiovascular function, but it cannot be measured directly by noninvasive means. Numerous attempts were made to derive P_a from noninvasively measured peripheral pressure (P_p) using mathematical transformations, transfer function or arterial system modeling approaches.  These techniques, in general, do not account for inter-subject or intra-subject variability. A few methods have recently been proposed to generate personalized adaptive transfer function employing arterial system modeling. However, these personalized models have to be calibrated across different patients at different times and the model algorithms are very sensitive to calibration technique and calibration error. More recently, multi-channel blind system identification (MBSI) have been implemented on these systems to mathematically derive common source P_a based on multiple P_p inputs. This method seems to afford self-calibrating and minimizes estimation error. In general, MBSI approaches are more convenient and practical for aortic pressure estimation, but have not been widely adopted.  In this thesis, the arterial system is proposed to be modeled as a Weiner System with linear finite impulse response (FIR) filter accounting for larger arteries transmission channel and non-linear memoryless function block accounting for all nonlinearities due to narrowing of arteries, branching and visco-elastic forces. This model is then experimentally validated with seven human blood pressure datasets.  Single input and multiple output (SIMO) or aortic-to-radial arterial transmission channel and aortic-to-femoral arterial transmission channel are established. To model the nonlinear memoryless monotonic function in the Wiener System model a correlation study is performed for linear finite impulse response (FIR) filter simulated peripheral pressure vs. measured peripheral pressure waveform.  Each of this correlation curves were fitted to linear, quadratic and cubic polynomial equation. It was found that Wiener model with 3rd order polynomial function yielded better modelling accuracy than that from 2nd order polynomial function which in turn was better than mere linear FIR filter.  P_a estimation technique is then presented by modeling arterial system as Multi-channel Weiner System. With this structure when pressure waveforms are measured from two distinct peripheral locations, multichannel blind system identification (MBSI) technique can be used to estimate common input pressure signal or P_a. Nonlinear MBSI method was employed on human blood pressure waveforms (7 datasets).  Results show P_a can be accurately derived. This method by nature is self-calibrating to account for any inter-personal, along with intra-personal, vascular dynamics inconstancy. Besides P_a estimation, the proposed MBSI method also allows extraction of system dynamics for vascular channels. Initially, linear finite impulse response (FIR) filter is assumed to be of fixed 10th order in the Wiener System model across all patient dataset. To further improve performance of this aortic pressure estimation method, a new and improved method is developed which estimates channel order preceding arterial system identification. By using effective channel order, system identification is optimized which then enhances aortic pressure estimation. Results showed significant improvement over our earlier method with far more accurate aortic pressure estimation. The outcome of the novel method as presented by this dissertation has the potential to enhance clinical diagnostic accuracy and subsequent treatment efficacy assessment.