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Design of a Novel Wearable Ultrasound Vest for Autonomous Monitoring of the Heart Using Machine Learning

Goodman, Garrett G.
http://rave.ohiolink.edu/etdc/view?acc_num=wright1607605597491118

Abstract Details

2020, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
As the population of older individuals increases worldwide, the number of people with cardiovascular issues and diseases is also increasing. The rate at which individuals in the United States of America and worldwide that succumb to Cardiovascular Disease (CVD) is rising as well. Approximately 2,303 Americans die to some form of CVD per day according to the American Heart Association. Furthermore, the Center for Disease Control and Prevention states that 647,000 Americans die yearly due to some form of CVD, which equates to one person every 37 seconds. Finally, the World Health Organization reports that the number one cause of death globally is from CVD in the form of either myocardial infarctions or strokes. The primary ways of assisting individuals affected with CVD are from either improved treatments, monitoring research, or primary and secondary prevention measures. In the form of cardiovascular structural monitoring, there are multiple ways of viewing the human heart. That is, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Computed Tomography (CT), and Ultrasonography are the four fundamental 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. Though, of the four methodologies, Ultrasonography allows for multiple configurations, is the least expensive, and has no detrimental side effects to the patient. Therefore, in an effort to improve continuous monitoring capabilities for cardiovascular health, we design a novel wearable ultrasound vest to create a near 3D model of the heart in real-time. Specifically, we provide a structural modeling approach specific to this system’s design via a Stereo Vision 3D modeling algorithm. Similarly, we introduce multiple Stochastic Petri Net (SPN) models of the heart for future functional feature extraction as well. Finally, this system also includes an individualized prediction methodology via our novel Machine Learning algorithm called the Constrained State Preserved Extreme Learning Machine (CSPELM) for heart state prediction. Thus, the wearable vest will not require continuous medical professional assistance and will allow for real-time autonomous monitoring of the heart. Furthermore, our wearable vest could be the entry point to objective monitoring for patients utilizing telemedicine.
Nikolaos G. Bourbakis, Ph.D. (Advisor)
Soon M. Chung, Ph.D. (Committee Member)
Yong Pei, Ph.D. (Committee Member)
Iosif Papadakis Ktistakis, Ph.D. (Committee Member)
Konstantina Nikita, Ph.D. (Committee Member)
Anthony Pothoulakis, M.D. (Other)
169 p.
Biomedical Engineering; Biomedical Research; Computer Science; Medical Imaging
medical imaging; ultrasound; ultrasonography; ultrasound transducers; computer vision; heart; cardiovascular; stereo vision; neural networks; modeling; machine learning; extreme learning machine; constrained state-preserved extreme learning machine

Recommended Citations

Citations

  • Goodman, G. G. (2020). Design of a Novel Wearable Ultrasound Vest for Autonomous Monitoring of the Heart Using Machine Learning [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1607605597491118

    APA Style (7th edition)

  • Goodman, Garrett. Design of a Novel Wearable Ultrasound Vest for Autonomous Monitoring of the Heart Using Machine Learning. 2020. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1607605597491118.

    MLA Style (8th edition)

  • Goodman, Garrett. "Design of a Novel Wearable Ultrasound Vest for Autonomous Monitoring of the Heart Using Machine Learning." Doctoral dissertation, Wright State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1607605597491118

    Chicago Manual of Style (17th edition)

wright1607605597491118
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This open access ETD is published by Wright State University and OhioLINK.