Detecting Heart Diseases using a Stethoscope-based Heart Sound Method.

Detecting heart diseases has been a research interest for centuries. Many of these techniques are based on stethoscope, but only a few of these are digitally analyzed. In our paper, we propose a new method to detect heart diseases by analyzing heart sounds. Our goal is to help the medical doctor to identify whether a patient has heart disease or not. In general, doctors use acoustic stethoscope to detect abnormalities in the heart sound and predict abnormal conditions of the human heart. One major problem is that the frequency range and intensity of the heart sound is very low. Moreover, there are different types of heart sounds indicating different types of heart diseases. Hence, doctors are facing difficulties while detecting the cardiac sound and its abnormalities. Even the expert doctors may fail sometime to analyze heart sound properly. We developed and applied a novel data analysis to detect heart problems. Our method uses deep architecture features for analyzing heart diseases. We consider the heart sound as our raw data. This approach uses electronic stethoscope also known as e-stethoscope (that is, electronic stethoscope) to collect heart sounds and deep learning approach to identify that a heart has any disease or is healthy. If the heart has a disease, then it is desirable to identify the disease type. It is aimed to design a software known as a heartbeat audio classifier. This software should be able to differentiate normal heartbeats and heart murmurs which would assist the doctors to analyze a heart sound and detect a disease condition of the heart. Though our approach is not perfect, it shows that our approach leads to better results in comparison with others. [ABSTRACT FROM AUTHOR]