Abstract

Each year, thousands of people die from heart disease and related illnesses due to the lack of available donor organs. Left ventricular assist devices (LVADs) aim to mitigate that occurrence, serving as a bridge-to-surgery option. While short term survival rates of LVAD patients near that of orthotopic surgery they are not viable long term options due to varied reasons. This work examines one cause, outlet graft thrombosis, and develops an algorithm for increasingly robust classification of device condition as it pertains to thrombosis or more generally occlusion. In order to do so an in vitro heart simulator is developed so that varying degrees of signal non-stationarity can be simulated and tested over a wide range of physiological blood pressure and heart rate conditions. Using a seeded-fault methodology, acoustics are acquired at the LVAD outlet graft location and subsequent spectral images of the sounds are developed. Statistical parameters from the images are used as features for classification using a support vector machine (SVM) which yields promising results. Given a comprehensive training space classification can be performed to fair accuracies (roughly 80%) using only the spectral image parameters. However, when the training space is limited augmenting the image features with patient state parameters elicits more robust identification. The algorithm developed in this work offers non-invasive diagnostic potential for LVAD conditions otherwise requiring invasive means.

Publication Date

8-2018

Document Type

Thesis

Student Type

Graduate

Degree Name

Mechanical Engineering (MS)

Department, Program, or Center

Mechanical Engineering (KGCOE)

Advisor

Jason Kolodziej

Advisor/Committee Member

Steven Day

Advisor/Committee Member

Mark Kempski

Campus

RIT – Main Campus

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