The art of selecting the ECG input in neural networks to classify heart diseases: a dual focus on maximizing information and reducing redundancy.

ABSTRACT:

Background and objectives

Accurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and its impact on classification tasks using Convolutional Neural Networks (CNNs) in cardiovascular diseases.

Methods

We employed signal theory and mutual information to introduce a novel redundancy metric and explored techniques for redundancy augmentation and reduction. This involved lead selection and transformation to evaluate the effects on neural network performance.

Results

Our findings indicate that optimizing input con

SUBMITTER: Ramirez E 

PROVIDER: S-EPMC11491564 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature