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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorSteensma, Bart
dc.contributor.authorMarc, Flavius
dc.date.accessioned2023-07-20T00:00:57Z
dc.date.available2023-07-20T00:00:57Z
dc.date.issued2023
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/44205
dc.description.abstractHeart failure, a condition impacting more than 23 million individuals globally, is anticipated to see an increase in its incidence. Among the various types of heart failure, Congestive Heart Failure (CHF) stands out with its hallmark of inadequate blood pumping leading to fluid accumulation in diverse body regions. Monitoring patients afflicted by CHF holds the key to better healthcare outcomes, which include timely medication delivery and reducing rehospitalization. This literature review discusses the feasibility of employing seismocardiography (SCG). SCG is a non-invasive method that relies on accelerometers to record the chest vibrations caused by cardiac activities. SCG's correlation with intracardiac filling pressures has the potential to monitor both healthy individuals and patients with heart failure, thereby reducing readmission. In addition, the integration of SCG with other wearable sensor technologies and machine learning applications can further improve diagnostic accuracy and personalized predictive models for HF progression.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectCongestive Heart Failure (CHF) is inadequate blood pumping leading to fluid accumulation in the body. Monitoring CHF patients is key, enabling timely medication and reducing rehospitalization. This literature review discusses the feasibility of employing seismocardiography to improve heart failure progression.
dc.titleFeasibility of monitoring congestive heart failure with seismocardiography: a literature review
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsseismocardiography; SCG; congestive heart failure; right heart catheterization; mechanical vibrations; filling pressure
dc.subject.courseuuMedical Imaging
dc.thesis.id19398


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