Arterial blood pressure estimation using ultrasound technology and transmission line arterial model
Author(s)
Beeks, Kyle A.
Download1102055570-MIT.pdf (6.358Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Charles G. Sodini.
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This thesis describes the application of a transmission line model to arterial measurements in order to derive useful cardiovascular parameters. Non-invasive ultrasound techniques are used to make these measurements, which has several benefits over invasive methods such as arterial catheterization. However, invasive methods are seen as the "gold standard" measurements and therefore the most accurate. Having accurate measurements that can be done non-invasively would be very desirable for cardiologists to determine their patients' risk of developing cardiovascular disease. This work details how to obtain the blood flow and pulse pressure waveforms using ultrasound transducers. Two transducers, one for imaging and one for Doppler, can be used together to derive these waveforms from distension and blood flow velocity measurements. Unfortunately, the only blood pressure waveform that can be obtained is the pulse pressure, which does not contain diastolic information. By decomposing the backward and forward pulse and flow waves and using the transmission line model, the diastolic pressure can be determined and the complete arterial blood pressure waveform can be obtained.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 67-69).
Date issued
2019Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.