Seismocardiography (SCG) is the low frequency chest surface vibration generated by the mechanical activities of the heart. SCG has been found to have clinical utilities in diagnosis of different cardiac diseases. The first part of this study focused on the application of SCG signal in predicting hospital readmissions of the heart failure (HF) patients. Conventional machine learning and deep learning models have been developed using SCG signal acquired from the HF patients. Early HF readmissions was predicted with decent accuracies with these models. This may potentially help the clinicians to identify the patients who need special care and treatment and make timely targeted interventions. This will ensure better management of HF patients and reduce the mortality rate. One of the limitations of using SCG signal in clinical settings is its variability. To investigate SCG variability, an exercise protocol has been developed. SCG signal was acquired from the healthy subjects when they underwent the protocol. It was found that cardiopulmonary interactions may contribute to the variability in SCG signal. The study results help to better understand the source of variability which eventually may increase the clinical utility of SCG signal. Another limitation of SCG signal is that it is highly sensitive to the ambient and locomotion-induced noises. This can distort the SCG signal. Hence, removal of noises is a necessary step to use SCG in ambulatory assessment of HF patients. To encounter this problem, a healthy subject performed different maneuvers to induce few common types of noises in the SCG signal. Different signal processing techniques have been employed to remove the noises from the signal. A comparison among different techniques has been provided which may lead to developing an algorithm in the future that is capable of autodetecting noises and suppress them.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1101 |
| Date | 01 January 2023 |
| Creators | Dhar, Rajkumar |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
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