SUBJECT-SPECIFIC MULTICHANNEL BLIND SYSTEM IDENTIFICATION OF HUMAN ARTERIAL TREE VIA CUFF OSCILLATION MEASUREMENTS

We developed and evaluated a mathematical model-based method to monitor cardiovascular health and estimate risk predictors from two peripheral cuff oscillation measurements. The model structure was established by studying tube-load models individually augmented with a gain, Voigt model, and standard linear solid model to best capture the relationship between carotid tonometry and cuff waveforms at the upper arm and ankle. The arm-cuff interface was better modeled with increasing viscoelasticity but not as much for the ankle-cuff interface. Next, model-estimated ankle blood pressure waveforms were used to formulate a matrix equation for estimating wave reflection. Subsequently derived risk predictors were adequately correlated with those from reference methods. Finally, subject-specific central blood pressure waveforms were estimated from two cuff oscillation signals via multichannel blind system identification. The model estimated central arterial blood pressure waveforms with good accuracy with a median RMSE of 3.08 mmHg and IQR of 1.71 mmHg

Dynamic Measures of Arterial Stiffness in a Rodent Model

Cardiovascular disease is one of the leading causes of death in Canada. Arterial stiffness is an important factor in the pathogenesis of cardiovascular disease. Cardiac failure, hypertension, renal failure, and dementia have all been linked to arterial stiffness. The arterial system is designed to dampen the pulses of blood from the heart's left ventricle and distribute the blood forward as steady flow in the small vessels. The pulse-dampening ability of the arterial system is reduced with age when the elastic fibers in the arterial wall degrade and fracture. The arterial stiffening process can accelerate from deposition of minerals within the arterial wall, such as calcium, from the endothelial layer becoming compromised or from fibrosis secondary to inflammation or turbulence. Arterial stiffness can be assessed post-mortem by microscopic examination of the arterial wall. However, for use in dynamic experiments and for therapeutic intervention, several ante-mortem techniques have been developed: pulse wave velocity (PWV), pulse waveform analysis (PWA), wave separation analysis (WSA), and carotid ultrasonography. Rats are important models for cardiovascular disease, toxicology, and pharmacological studies because of their convenient size and short life cycle. However, PWA and WSA have not been shown to be valid approaches for studying arterial stiffness in rat peripheral arteries. In this thesis, dynamic in vivo methods for PWA and WSA in rat peripheral arteries were developed to provide accurate measures of arterial stiffness. Software specific to the rat vasculature, PWanalyze and WSanalyze, was developed to measure PWA and WSA parameters, respectively. A comparison of these PWA and WSA methods in rat peripheral arteries was performed by creating a range of arterial stiffnesses through acute and chronic experiments. Arterial stiffness was measured in the femoral artery by a novel PWA parameter, the minimum time derivative of blood pressure dp/dt(min), as effectively as the established parameter the maximum time derivative of blood pressure dp/dt(max). A new method of WSA in femoral arteries was developed. Backward wave amplitude measured in the aorta was shown to increase as arteries stiffened and decrease as arteries relaxed with acute vasoactive drug injections. These experiments showed that dp/dt(min) and WSA are valid approaches to use when studying arterial stiffness in rats

AN ACTIVE NON-INTRUSIVE SYSTEM IDENTIFICATION APPROACH FOR CARDIOVASCULAR HEALTH MONITORING

In this study a novel active non-intrusive system identification paradigm is developed for the purpose of cardiovascular health monitoring. The proposed approach seeks to utilize a collocated actuator sensor unit devised from the common blood pressure cuff to simultaneously 1) produce rich transmural blood pressure waves that propagate through the cardiovascular system and 2) to make measurements of these rich peripheral transmural blood pressures utilizing the pressure oscillations produced within the cuffs bladder in order to reproduce the central aortic blood pressure accurately. To achieve this end a mathematical model of the cardiovascular system is developed to model the wave propagation dynamics of the external (excitation applied by the cuff) and internal (excitation produced by the heart) blood pressure waveforms through the cardiovascular system. Next a system identification protocol is developed in which rich transmural blood pressures are recorded and used to identify the parameters characterizing the model. The peripheral blood pressures are used in tandem with the characterized model to reconstruct the central aortic blood pressure waveform. The results of this study indicate the developed protocol can reliably and accurately reproduced the central aortic blood pressure and that it can outperform its intrusive passive counterpart (the Individualized Transfer Function methodology). The root-mean-square error in waveform reproduction, pulse pressure error and systolic pressure errors were evaluated to be 3.31 mmHg, 1.36 mmHg and 0.06 mmHg respectively for the active nonintrusive methodology while for the passive intrusive counterpart the same errors were evaluated to be 4.12 mmHg, 1.59 mmHg and 2.67 mmHg indicating the superiority of the proposed approach

SYSTEM IDENTIFICATION AND DE-CONVOLUTION OF A CLASS OF MULTI-CHANNEL WAVE PROPAGATION SYSTEMS FOR UNOBTRUSIVE CARDIOVASCULAR HEALTH MONITORING

The main goal of this thesis is to improve the cardiovascular health monitoring by developing a novel model-based blind system identification approach. This research lies on the core idea that the central aortic blood pressure (BP) waveform can be estimated from as few as two non-invasive circulatory signals. To achieve this goal, first, we formulated a physiological model for the class of multi-channel systems with non-invasive BP measurements and expressed it as a blind system identification problem. We verified this model for estimating the central blood pressure waveform from pulse volume records (PVR) signals from arm and leg, collected from 10 human subjects. The results showed that the proposed approach could estimate central aortic blood pressure waveform accurately. The average root-mean-squared error associated with the central aortic blood pressure waveform was 4.1 mmHg while the average errors associated with central aortic systolic and pulse pressures were 2.4 mmHg and 2.0 mmHg respectively. Afterward, we compared this method with a population-based technique to calculate cardiovascular risk predictors. First, we used the same approach to estimate the central blood pressure waveform from two non-invasive peripheral waveforms and then, calculated cardiovascular risk predictors. Experimental results obtained from 164 human subjects with a wide blood pressure range showed that this approach could estimate cardiovascular risk predictors accurately. Further analysis showed that the suggested approach outperformed a generalized transfer function regardless of the degree of pulse pressure amplification, but especially in high and low amplification ranges. Finally, a new closed-loop approach to input de-convolution in coprime multi-channel systems based on state estimation techniques is proposed. This approach is based on the idea that the unknown input signal in a multi-channel system may be regarded as a state variable to be estimated from multiple output signals of the system. The validity and potential of the approach were illustrated using the clinically significant case study of estimating central aortic BP waveform from two non-invasively peripheral arterial pulse waveforms. The proposed algorithm could reduce the root-mean-squared error associated with the central aortic blood pressure by up to 27.5% and 28.8% relative to two conventional central aortic blood pressure estimation techniques: open-loop inverse filtering and peripheral arterial pulse waveforms scaled to central aortic diastolic and mean pressures