Automated Diagnosis of Heart Valve Diseases from Phonocardiogram Signals using Deep Learning

Abstract

Heart valve diseases (HVDs) are the primary causes of mortality in developing and underdeveloped countries. Early detection of HVDs is essential to avoid lethal heart diseases due to the disease’s progression. Phonocardiogram (PCG) signal provides a non-invasive and cost-effective tool that helps with the preliminary diagnosis of HVDs. However, the raw PCG signals are often susceptible to noise and artifacts. It degrades the signal quality and makes it challenging to diagnose HVDs manually. Furthermore, the wide variabilities in the PCG morphologies due to HVDs exhibit manual examination, often subjective and prone to human error. To address the above challenges, this dissertation focuses on developing automated deep-learning methods for diagnosing HVDs.