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Ph.D. Dissertation Proposal Defense A Novel Wearable Ultrasound Vest with Individualized Capabilities of Near 3D Modeling and Real-Time Monitoring of the Heart By Garrett Goodman

Monday, December 16, 2019, 10 am to Noon
Campus: 
Dayton
467 Joshi
Audience: 
Current Students
Faculty
Staff

Ph.D. Committee:  Drs. Nikolaos Bourbakis (advisor), Soon Chung, Yong Pei, Konstantina Nikita (National Technical University of Athens), and Iosif Papadakis Ktistakis (ASML)

ABSTRACT:

As the population of older individuals increases worldwide, the number of people with cardiovascular issues and diseases has also increased. The rate at which individuals in America and worldwide that succumb to cardiovascular disease is rising as well. That is, approximately 2,303 Americans die to some form of cardiovascular disease per day according to the American Heart Association. Furthermore, the World Health Organization reports that the number one cause of death globally is from cardiovascular disease in the form of either myocardial infarctions or strokes.

A way of helping these individuals is from continued treatment and monitoring research. In the form of cardiovascular monitoring, there are multiple different ways of viewing the human heart. That is, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Computed Tomography (CT), and Ultrasonography are the four primary imaging techniques. Though, continuous monitoring with these imaging techniques is far from currently possible. Large financial cost and size (MRI), radiation exposure (PET and CT), or necessary physician assistance (Ultrasonography) are the current primary problems.

Therefore, in an effort to improve the continuous monitoring capabilities for cardiovascular health, we propose a novel wearable ultrasound vest to create a near 3D model of the heart in real time. The 3D modeling will be created using a Stereo Vision 3D modeling algorithm. This system also includes an individualized prediction methodology via our novel Machine Learning algorithm called the Constrained State Preserved Extreme Learning Machine (CSPELM). The wearable vest will not require continuous medical professional assistance and will allow for real time autonomous monitoring of cardiovascular health.

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