An Investigation of the Relationship Between Respiration and Seismocardiographic Signals Using Signal Processing, Machine Learning and Finite Element Analysis
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| Title: | An Investigation of the Relationship Between Respiration and Seismocardiographic Signals Using Signal Processing, Machine Learning and Finite Element Analysis |
|---|---|
| Authors: | Hassan, Tanvir |
| Committee Members: | Mansy, Hansen |
| Summary: | Cardiovascular disease (CVD) is one of the major causes of death worldwide. Disease management, as well as patient health, can be significantly improved by early detection of patient deterioration and proper intervention. Review of the patient's medical history and physical examination including stethoscope auscultation and electrocardiograms (ECG), echocardiography imaging, numerous blood testing, and computed tomography are common means of evaluating cardiac function. Seismocardiographic (SCG) signals are the vibrations of the chest wall due to the mechanical activity of the heart. These signals can provide useful information about heart function and could be used to diagnose cardiac problems. The variability in SCG waveforms may make it difficult to obtain accurate waveforms, limiting SCG clinical value. Breathing is a well-known source of change in SCG morphology. In this dissertation, SCG variability due to respiration is described, related signal characteristics changes are measured, and the effects of breathing states and maneuvers are discussed. Increased SCG variability understanding can aid in accounting for variability in signal as well as more accurate characterization of significant features in SCG that could correlate with heart health. Direct airflow measurement is frequently used to assess respiration. When direct airflow access is difficult or unavailable, indirect ways to breathing monitoring might be used. The seismocardiographic signal is influenced by respiration. As a result, this signal can be utilized to noninvasively determine the respiratory phases. Hence, SCG may reduce the requirement for direct airflow measurements in situations where SCG signals are easily available. This dissertation extracts respiration derived from SCG in healthy adults using machine learning techniques and compares the results with direct respiration airflow measurements. Finite element method (FEM) was implemented to perform SCG simulation during different breathing states by modeling the myocardial movements propagation to the surface of the chest. SCG waveforms predicted by FEM analysis were comparable with SCG signals measured at the surface of the chest suggesting that myocardial activity is the SCG main source. The effects of increased soft tissue in the chest wall on SCG signal were investigated and were found to decrease SCG amplitude. The research led to an enhanced understanding of the SCG variability sources as well as respiratory phase-detection methods. These discoveries could lead to better non-invasive, low-cost approaches development for managing cardiovascular disorders, which can enhance patient quality of life. |
| URL: | https://stars.library.ucf.edu/etd2020/1022 |
| Database: | OpenDissertations |
| FullText | Text: Availability: 0 |
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| Header | DbId: ddu DbLabel: OpenDissertations An: ddu.oai.stars.library.ucf.edu.etd2020.2021 AccessLevel: 6 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Investigation of the Relationship Between Respiration and Seismocardiographic Signals Using Signal Processing, Machine Learning and Finite Element Analysis – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hassan%2C+Tanvir%22">Hassan, Tanvir</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: Cardiovascular disease (CVD) is one of the major causes of death worldwide. Disease management, as well as patient health, can be significantly improved by early detection of patient deterioration and proper intervention. Review of the patient's medical history and physical examination including stethoscope auscultation and electrocardiograms (ECG), echocardiography imaging, numerous blood testing, and computed tomography are common means of evaluating cardiac function. Seismocardiographic (SCG) signals are the vibrations of the chest wall due to the mechanical activity of the heart. These signals can provide useful information about heart function and could be used to diagnose cardiac problems. The variability in SCG waveforms may make it difficult to obtain accurate waveforms, limiting SCG clinical value. Breathing is a well-known source of change in SCG morphology. In this dissertation, SCG variability due to respiration is described, related signal characteristics changes are measured, and the effects of breathing states and maneuvers are discussed. Increased SCG variability understanding can aid in accounting for variability in signal as well as more accurate characterization of significant features in SCG that could correlate with heart health. Direct airflow measurement is frequently used to assess respiration. When direct airflow access is difficult or unavailable, indirect ways to breathing monitoring might be used. The seismocardiographic signal is influenced by respiration. As a result, this signal can be utilized to noninvasively determine the respiratory phases. Hence, SCG may reduce the requirement for direct airflow measurements in situations where SCG signals are easily available. This dissertation extracts respiration derived from SCG in healthy adults using machine learning techniques and compares the results with direct respiration airflow measurements. Finite element method (FEM) was implemented to perform SCG simulation during different breathing states by modeling the myocardial movements propagation to the surface of the chest. SCG waveforms predicted by FEM analysis were comparable with SCG signals measured at the surface of the chest suggesting that myocardial activity is the SCG main source. The effects of increased soft tissue in the chest wall on SCG signal were investigated and were found to decrease SCG amplitude. The research led to an enhanced understanding of the SCG variability sources as well as respiratory phase-detection methods. These discoveries could lead to better non-invasive, low-cost approaches development for managing cardiovascular disorders, which can enhance patient quality of life. – Name: URL Label: URL Group: URL Data: <link linkTarget="URL" linkTerm="https://stars.library.ucf.edu/etd2020/1022" linkWindow="_blank">https://stars.library.ucf.edu/etd2020/1022</link> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ddu&AN=ddu.oai.stars.library.ucf.edu.etd2020.2021 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English Subjects: – SubjectFull: Seismocardiography; Machine learning; Finite element analysis; Respiration monitoring; Cardiovascular disease Type: general – SubjectFull: Cardiovascular Diseases Type: general – SubjectFull: Mechanical Engineering Type: general – SubjectFull: Cardiography--Mathematical models; Heart--Diseases--Diagnosis--Data processing; Cardiovascular system--Simulation methods; Respiration--Research; Cardiovascular system--Diseases--Diagnosis--Instruments--Technological innovations Type: general Titles: – TitleFull: An Investigation of the Relationship Between Respiration and Seismocardiographic Signals Using Signal Processing, Machine Learning and Finite Element Analysis Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hassan, Tanvir IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2022 |
| ResultId | 1 |
