Transfer Learning Benchmark for Cardiovascular Disease Recognition

Abstract

The cardiac auscultation using the classical stethoscope (PCG: phonological cardiogram) is known as the most famous method to detect Cardiovascular Disease (CVD). However, this exam requires a qualified cardiologist which relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in heart during the pumping action. Many research works have been conducted for detecting CVD from PCG signals by using public and private datasets. Due to the lack of CVD recognition benchmark, classification results are very heterogeneous and can not be compared objectively. In this paper, we apply transfer learning to Pascal public dataset and provide an experimental benchmark without any denoising or cleaning steps. The main goal is to generate a set of experimental results which can be used as starting reference for future CVD recognition research based on PCG.