An intelligent support system is needed to assist in the identification of abnormalities of a
human heart. The integration of signal processing with machine learning techniques is a …

Objective: Heart abnormality detection using heart sound signals (phonocardiogram (PCG))
has been an active research area for the last few decades. In this paper, automatic heart …

Given the patient to doctor ratio of 50,000: 1 in low income and middle-income countries,
there is a need for automated heart sound classification system that can screen the …

Heart sound and its recorded signal which is known as phonocardiograph (PCG) are one of
the most important biosignals that can be used to diagnose cardiac diseases alongside …

This research pertains to classification of the heart sound using digital Phonocardiogram
(PCG) signals targeted to screen for heart ailments. In this study, an existing variant of the …

Heart sound analysis has been a major topic of research over the past few decades.
However, the necessity for a large and reliable database has been a major concern in these …

As cardiovascular disorders are prevalent, there is a growing demand for reliable and
precise diagnostic methods within this domain. Audio signal-based heart disease detection …

The future of quick and efficient disease diagnosis lays in the development of reliable non-
invasive methods. As for the cardiac diseases–one of the major causes of death around the …

We purpose a novel method that combines modified frequency slice wavelet transform
(MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal …

Background Heart sound plays a vital role to achieve an accurate diagnosis of
cardiovascular diseases, and its auxiliary diagnosis methods have become a hotspot. Aim …