Seismocardiography - Genesis, and Utilization of Machine Learning for Variability Reduction and Improved Cardiac Health Monitoring
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| Title: | Seismocardiography - Genesis, and Utilization of Machine Learning for Variability Reduction and Improved Cardiac Health Monitoring |
|---|---|
| Authors: | Thibbotuwawa Gamage, Peshala |
| Committee Members: | Mansy, Hansen |
| Summary: | Seismocardiography (SCG) is the measured chest surface vibrations resulting from cardiac activity. Although SCG can contain information that correlate with cardiac health, its utility may be limited by lack of understanding of the signal genesis and a variability that can mask subtle SCG changes. The current research utilized medical imaging reconstruction and finite element method (FEM) to simulate SCG by modeling the propagation of myocardial movements to the chest surface. FEM analysis provided a link between myocardial movements and the SCG signals measured at the chest surface and suggested that myocardial movement is a primary source of SCG. Increased understanding of the sources and propagation of SCG may help increase the utility of SCG as a cardiac monitoring tool. To reduce the variability of SCG measured in human subjects, unsupervised machine learning (ML) was implemented to group SCG beats into clusters with minimal intra-cluster heterogeneity. The clustering helped reduce the SCG variability and unveiled consistent relations with the respiratory phases and SCG morphology. This clustering reduced noise in calculating signal features and provided additional useful features. The study also analyzed longitudinal SCG from heart failure (HF) patients in order to predict HF readmission. Here, many time- and frequency-domain SCG features were extracted. Certain features showed good correlations with readmission. Using supervised ML algorithms, high classification accuracies (up to 100%) were achieved suggesting high SCG utility for monitoring HF patients and possibly other heart conditions. Effective monitoring followed by timely intervention can lead to improved patient management and reduced mortality. |
| URL: | https://stars.library.ucf.edu/etd2020/622 |
| Database: | OpenDissertations |
| FullText | Text: Availability: 0 |
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| Header | DbId: ddu DbLabel: OpenDissertations An: ddu.oai.stars.library.ucf.edu.etd2020.1621 AccessLevel: 6 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Seismocardiography - Genesis, and Utilization of Machine Learning for Variability Reduction and Improved Cardiac Health Monitoring – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Thibbotuwawa+Gamage%2C+Peshala%22">Thibbotuwawa Gamage, Peshala</searchLink> – Name: Author Label: Committee Members Group: Au Data: <searchLink fieldCode="CO" term="%22Mansy%2C+Hansen%22">Mansy, Hansen</searchLink> – Name: Abstract Label: Summary Group: Ab Data: Seismocardiography (SCG) is the measured chest surface vibrations resulting from cardiac activity. Although SCG can contain information that correlate with cardiac health, its utility may be limited by lack of understanding of the signal genesis and a variability that can mask subtle SCG changes. The current research utilized medical imaging reconstruction and finite element method (FEM) to simulate SCG by modeling the propagation of myocardial movements to the chest surface. FEM analysis provided a link between myocardial movements and the SCG signals measured at the chest surface and suggested that myocardial movement is a primary source of SCG. Increased understanding of the sources and propagation of SCG may help increase the utility of SCG as a cardiac monitoring tool. To reduce the variability of SCG measured in human subjects, unsupervised machine learning (ML) was implemented to group SCG beats into clusters with minimal intra-cluster heterogeneity. The clustering helped reduce the SCG variability and unveiled consistent relations with the respiratory phases and SCG morphology. This clustering reduced noise in calculating signal features and provided additional useful features. The study also analyzed longitudinal SCG from heart failure (HF) patients in order to predict HF readmission. Here, many time- and frequency-domain SCG features were extracted. Certain features showed good correlations with readmission. Using supervised ML algorithms, high classification accuracies (up to 100%) were achieved suggesting high SCG utility for monitoring HF patients and possibly other heart conditions. Effective monitoring followed by timely intervention can lead to improved patient management and reduced mortality. – Name: URL Label: URL Group: URL Data: <link linkTarget="URL" linkTerm="https://stars.library.ucf.edu/etd2020/622" linkWindow="_blank">https://stars.library.ucf.edu/etd2020/622</link> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ddu&AN=ddu.oai.stars.library.ucf.edu.etd2020.1621 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English Subjects: – SubjectFull: Seismocardiography; Machine learning; Cardiac health monitoring; Variability reduction; Heart failure prediction Type: general – SubjectFull: Biomechanical Engineering Type: general – SubjectFull: Mechanical Engineering Type: general – SubjectFull: Cardiology--Data processing; Cardiography--Data processing; Cardiovascular system--Research; Artificial intelligence--Medical applications--Research; Heart--Diseases--Diagnosis--Data processing Type: general Titles: – TitleFull: Seismocardiography - Genesis, and Utilization of Machine Learning for Variability Reduction and Improved Cardiac Health Monitoring Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Thibbotuwawa Gamage, Peshala IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2020 |
| ResultId | 1 |
