ORCID Identifier(s)

0000-0002-5423-043X

Graduation Semester and Year

2021

Language

English

Document Type

Thesis

Degree Name

Master of Science in Mechanical Engineering

Department

Mechanical and Aerospace Engineering

First Advisor

David A Hullender

Second Advisor

Seiichi Nomura

Abstract

Cardiovascular diseases are the leading cause of mortalities worldwide as well as in the United States. Early diagnosis and prediction of these diseases is critical to mitigate the risks to the patient. A common method to assess a person’s heart health is through the use of cuff type oscillometric devices placed on the arm of the patient. As the mean pressure in the cuff is increased the blood flow in the brachial artery becomes blocked. When the cuff pressure is reduced the blood starts flowing again; during this cycle, pressure fluctuations in the cuff are measured using a pressure transducer. Modern devices use proprietary algorithms on these pressure fluctuations to estimate a patient’s systolic and diastolic pressure levels. Hullender and Brown developed a model and an extended Kalman filter algorithm which is able to estimate the total blood pressure waveform along with arterial stiffness component using the same cuff pressure fluctuations. This research pertains to an evaluation of the algorithm for different levels of measurement uncertainty and different mean pressure cuff levels and cycle rates. This work also develops and evaluates an extension of the algorithm to assess the cardiovascular health of a patient. The extended algorithm uses standardized values for a blood pressure waveform to diagnose cardiovascular anomalies such as high blood pressure, hypertension severity, arrhythmia, atrial fibrillation, tachycardia and bradycardia. It also extracts important blood pressure waveform characteristics such as time periods and their ratios associated with different peaks in each cycle, dicrotic notch depth, systolic pressure increment and detriment slopes along with ventricular and total cardiac output factors. Furthermore, these characteristics are measured continuously and their average and variations are provided in the final diagnostic report. While the measured characteristics are not as obvious as the diagnosed anomalies, they can be viewed by a medical professional for quick diagnosis instead of having to analyze each parameter by studying the waveform. The significance of these preliminary results to assess the cardiovascular health of a patient appears to be most promising and justification for future research considering complexities as- sociated with patient-to-patient differences, levels of patient activity, effects of other medical issues, etc.

Keywords

Cardiovascular diagnosis, Extended Kalman filter, Waveform analysis, Blood pressure waveform, Optimal control, System testing, Patient monitoring

Disciplines

Aerospace Engineering | Engineering | Mechanical Engineering

Comments

Degree granted by The University of Texas at Arlington

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