Evaluation of Deep Learning Methods (DnCNN and U-Net) for Denoising of Heart Auscultation Signals

Every year, around 17.9 million people die due to Cardiovascular Diseases which is 31% of global deaths. These numbers indicate the need for a system that should be sensitive to detect Heart Disease at an early stage. Heart sound signals can give information about heart damage at a much earlier stage. For proper information extraction from heart auscultation about a heart condition, it is required that the signals are free from noise so that improper classification as the normal abnormal situation can be eliminated. A number of denoising methods have been proposed for denoising of heart auscultation sounds, both in the time domain and frequency domain. Most of them suffer from one or more problems to properly denoise the heart sound. DnCNN and U-Net have been used previously as a state of art method to denoise images. In this paper, we try to evaluate the ability of DnCNN and U-Net to denoise the signal.