Clinically interpretable multiclass neural network for discriminating cardiac diseases.

ABSTRACT:

Background

Deep-learning applications in cardiology typically perform trivial binary classification and are able to discriminate between subjects affected or not affected by a specific cardiac disease. However, this working scenario is very different from the real one, where clinicians are required to recognize the occurrence of one cardiac disease among the several possible ones, performing a multiclass classification. The present work aims to create a new interpretable deep-learning tool able to perform a multiclass classification and, thus, discriminate among several different cardiac diseases.

Methods

The "China Physiological Signal Challenge in 2018" Physionet database was used to develop a multiclass neural network, constructed by the Advanced Repeated Structuring & Le

SUBMITTER: Sbrollini A 

PROVIDER: S-EPMC11742852 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature