Deep learning fusion framework for automated coronary artery disease detection using raw heart sound signals.

ABSTRACT: One of the most common cardiovascular diseases is coronary artery disease (CAD). Thus, it is crucial for early CAD diagnosis to control disease progression. Computer-aided CAD detection often converts heart sounds into graphics for analysis. However, this method relies heavily on the subjective experience of experts. Therefore, in this study, we proposed a method for CAD detection using raw heart sound signals by constructing a fusion framework with two CAD detection models: a multidomain feature model and a medical multidomain feature fusion model. We collected heart sound signal datasets from 400 participants, extracting 206 multidomain features and 126 medical multidomain features. The designed framework fused the same one-dimensional deep learning features with different multidomain fe

SUBMITTER: Dai Y 

PROVIDER: S-EPMC11388508 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature