Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …

Heart sound auscultation has been applied in clinical usage for early screening of
cardiovascular diseases. Due to the high demand for auscultation expertise, automatic …

In the undertaken study, we have used a customized dataset termed``Cardiac-200''and the
benchmark dataset``PhysioNet.''which contains 1500 heartbeat acoustic event samples …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

Cardiovascular diseases are the leading cause of death and severely threaten human
health in daily life. There have been dramatically increasing demands from both the clinical …

Traditionally, abnormal heart sound classification is framed as a three-stage process. The
first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; …

Transformer architectures have become increasingly popular in healthcare applications.
Through outstanding performance in natural language processing and superior capability to …

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 …

In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019
(COVID-19) has raised a real threat to human lives owing to infection on lung cells and …

Automatic heart sound abnormality detection plays a vital role in the preliminary diagnosis of
cardiovascular diseases (CVDs). Many handcraft-designed or learning-based methods have …