Deep learning of heart-sound signals for efficient prediction of obstructive coronary artery disease.

ABSTRACT:

Background

Due to the limitations of current methods for detecting obstructive coronary artery disease (CAD), many individuals are mistakenly or unnecessarily referred for coronary angiography (CAG).

Objectives

Our goal is to create a comprehensive database of heart sounds in CAD and develop accurate deep learning algorithms to efficiently detect obstructive CAD based on heart sound signals. This will enable effective screening before undergoing CAG.

Methods

We included 320 subjects suspected of CAD who underwent CAG. We employed advanced filtering techniques and state-of-the-art deep learning models (VGG-16, 1D CNN, and ResNet18) to analyze the heart sound signals and identify obstructive CAD (defined as at least one ≥50 % stenosis). To assess the performance of our

SUBMITTER: Ainiwaer A 

PROVIDER: S-EPMC10758826 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature