A novel 1-D densely connected feature selection convolutional neural network for heart sounds classification.

ABSTRACT:

Background

Heart sound auscultation, due to it being a non-invasive, convenient, and relatively low-cost technique, remains the dominant method for detection of cardiovascular disease.

Methods

In this paper, we present a method for identifying abnormal heart sounds based on a novel Dense Feature Selection Convolution Network framework (Dense-FSNet). The Dense-FSNet is comprised of multiple, circular dense connectivity modules, called Clique Blocks. These Clique Blocks can allow low-level and high-level features to stimulate each other for cyclic updates, which subsequently enhances the information flow among layers. Inspired by the channel-wise attention mechanism from Squeeze-and-Excitation Networks (SENet), we adopted squeeze-and-excitation block to avoid the progressive g

SUBMITTER: Zhou X 

PROVIDER: S-EPMC8756246 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature