Physics-Based Model-Guided Machine Learning Analysis of Wrist Ballistocardiography for Cuff-Less Blood Pressure Monitoring

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dc.contributor.advisorHahn, Jin Ohen_US
dc.contributor.authorYousefian, Peymanen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2019-06-19T05:41:26Z
dc.date.available2019-06-19T05:41:26Z
dc.date.issued2019en_US
dc.description.abstractCuff-less blood pressure (BP) monitoring technology is being widely pursued today. In this research we investigated the wrist ballistocardiogram (BCG) as a limb BCG, to develop a scientific basis to use the limb BCG to for cuff-less BP monitoring. In our study, we pursue two alternative approaches to the use of wrist BCG signal for BP monitoring: (1) use of the wrist BCG as proximal timing in pulse transit time (PTT) based methods; (2) use of wrist BCG wave features for BP monitoring. In this regard, the physics-based model is developed to elucidate the mechanism responsible for the generation of the BCG signal at the body’s extremity limb locations. The developed and experimentally validated mathematical model can predict the limb BCG in responses to the arterial BP waves in the aorta. The model suggests that the limb BCG waveform reveals the timings and amplitudes associated with the aortic BP waves and it exhibits meaningful morphological changes in response to the alterations in the CV risk predictors. Such understanding combined with machine learning techniques has helped us to extract viable features, and construct predictive models that can estimate BP. The findings of this study show that limb BCG has the potential to realize convenient cuff-less BP monitoring. First, it is a strong candidate to extract the proximal timing for PTT based methods. Second, BCG wave features are associated with BP and it could be used for BP monitoring. Third, we can combine the PTT with BCG wave features to achieve more comprehensive prediction models with superior performance.en_US
dc.identifierhttps://doi.org/10.13016/fhur-9bay
dc.identifier.urihttp://hdl.handle.net/1903/21947
dc.language.isoenen_US
dc.subject.pqcontrolledBiomedical engineeringen_US
dc.subject.pquncontrolledArtificial Intelligenceen_US
dc.subject.pquncontrolledBiomedicalen_US
dc.subject.pquncontrolledBlood Pressureen_US
dc.subject.pquncontrolledHealth Monitoringen_US
dc.subject.pquncontrolledMachine learningen_US
dc.subject.pquncontrolledSmart Watchen_US
dc.titlePhysics-Based Model-Guided Machine Learning Analysis of Wrist Ballistocardiography for Cuff-Less Blood Pressure Monitoringen_US
dc.typeDissertationen_US

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