Heart sounds classification using deep learning algorithms

The ever-evolving medical field driven by the applications of deep learning is changing the practice in many ways. Heart sounds are critical aspects that have to do with cardiac disease diagnosis. These are techniques in which heart abnormalities can be detected using signals gotten from heart sounds. The eminence of heart disease in the world today makes a cardiologist a very important health professional. Likewise, the role of artificial intelligence in the treatment of the cardiovascular system - which includes the heart, the blood vessels, etc. Such systems assist cardiologists in promoting heart health in patients. The classic diagnostic method is through cardiac auscultation to detect abnormalities in heart sounds. Thus, the accuracy of the heart auscultation is highly important in the diagnostic process to screen outpatients with or without heart diseases. This made it obvious there is a need for a more accurate and precise computer-aided diagnosis (CAD) system to further assist cardiologists in the field by examining heart sounds to provide a detailed outline. In this study, deep learning algorithms are implemented for the detection of abnormal and normal heart sounds using classification techniques. This study outlines the overview of deep learning-based heart diseases diagnosis in the context of experimental research. Furthermore, this study highlights research work that indicates the advances in the application of deep learning technology in medical sciences.