ECG-GraphNet: Advanced arrhythmia classification based on graph convolutional networks.

ABSTRACT:

Background

Deep learning has significantly improved medical diagnostics, particularly in electrocardiogram (ECG) analysis, yet accurate classification of arrhythmias remains challenging.

Objective

We propose Electrocardiogram Graph Convolutional Network (ECG-GraphNet), a graph convolutional network designed to classify arrhythmias into 3 types: normal (N), supraventricular ectopic (S), and ventricular ectopic (V) beats.

Methods

ECG-GraphNet utilizes a novel graph representation of ECG data in which the P wave, QRS complex, and T wave are modeled as individual nodes. A unique QRS-centered weighted average pooling method is employed to enhance beat-specific feature extraction. We systematically explored various aspects including node features, edge definitions, a data a

SUBMITTER: Lee M 

PROVIDER: S-EPMC12411959 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature