Abstract

Right-heart catheterization is the most accurate method for estimating pulmonary artery pressure (PAP). Because it is an invasive procedure it is expensive, exposes patients to the risk of infection, and is not suited for long-term monitoring situations. Medical researchers have shown that PAP influences the characteristics of heart sounds. This suggests that heart sound analysis is a potential noninvasive solution to the PAP estimation problem. This thesis describes the development of a prototype system, called PAPEr, which estimates PAP noninvasively using heart sound analysis. PAPEr uses patient data with machine learning algorithms to build models of how PAP affects heart sounds. Data from 20 patients was used to build the models and data from another 31 patients was used as a validation set. PAPEr diagnosed these 31 patients for pulmonary hypertension with an accuracy of 77 percent.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2009-12-07

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd3329

Keywords

machine learning, pulmonary artery pressure estimation, feature selection

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