Remote measurements of heart valve sounds for health assessment and biometric identification

Cester, Lucrezia Maria Elisabetta (2022) Remote measurements of heart valve sounds for health assessment and biometric identification. PhD thesis, University of Glasgow.

Abstract

Heart failure will contribute to the death of one in three people who read this thesis; and one in three of those who don't.

Although in order to diagnose patients’ heart condition cardiologists have access to electrocardiograms, chest X-rays, ultrasound imaging, MRI, Doppler techniques, angiography, and transesophageal echocardiography, these diagnostic techniques require a cardiologist’s visit, are expensive, the examination time is long and so are the waiting lists. Furthermore abnormal events might be sporadic and thus constant monitoring would be needed to avoid fatalities.

Therefore in this thesis we propose a cost effective device which can constantly monitor the heart condition based on the principles of phonocardiography, which is a cost-effective method which records heart sounds.

Manual auscultation is not widely used to diagnose because it requires considerable training, it relies on the hearing abilities of the clinician and specificity and sensitivity for manual auscultation are low since results are qualitative and not reproducible. However we propose a cheap laser-based device which is contactless and can constantly monitor patients’ heart sounds with a better SNR than the digital stethoscope. We also propose a Machine Learning (ML) aided software trained on data acquired with our device which can classify healthy from unhealthy heart sounds and can perform biometric authentication. This device might allow development of gadgets for remote monitoring of cardiovascular health in different settings.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: Q Science > QB Astronomy; Q Science > QC Physics
• Colleges/Schools: College of Science and Engineering > School of Physics and Astronomy
• Supervisor's Name: Faccio, Professor Daniele
• Date of Award: 2022
• Depositing User: Theses Team
• Unique ID: glathesis:2022-83048
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 07 Sep 2022 09:17
• Last Modified: 07 Sep 2022 09:17
• Thesis DOI: 10.5525/gla.thesis.83048
• URI: http://theses.gla.ac.uk/id/eprint/83048

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