This paper represents an unsupervised approach to detect the positions of S1, S2 heart
sound events in a Phonocardiogram (PCG) recording. Insufficiency of correctly annotated …

Cardiovascular diseases have surpassed cancer as the leading cause of death on the
planet today. Numerous decision‐making systems with computer‐assisted support have …

Heart murmurs are the first signs of cardiac valve disorders. Several studies have been
conducted in recent years to automatically differentiate normal heart sounds, from heart …

Cardiovascular disorders (CVDs) have attracted a great deal of attention given that they are
one of the most common causes of mortality every year. With early diagnoses, CVDs can be …

One of humans' auditory abilities is differentiation between sounds with slightly different
frequencies. Recently, the auditory image model (AIM) was developed to numerically …

Segmentation of the phonocardiography (PCG) signal is of importance for diagnosis of heart
health and the same is recently reported to be done with tunable quality Wavelet transform …

Past decades witnessed the expansion of linear signal processing methods in numerous
biomedical applications. However, the nonlinear behavior of biomedical signals revived the …

This paper presents a study of murmur recognition in heart sound signal with different
machine learning algorithms using combined features. The heart sound signal is required to …

Detection of valvular heart disease from phonocardiogram (PCG) signal is an important non-
invasive and low-cost tool that can have a big impact on the healthcare market. We have …

Phonocardiogram (PCG) signals, their feature extraction and segmentation to diagnose
several heart disorders and developing detection models is the primary area of research …