Principal component conditional generative adversarial networks for imbalanced ECG classification enhancement.

ABSTRACT: With over a century of development, electrocardiogram (ECG) diagnostics has become the preferred tool for healthcare professionals in cardiovascular disease diagnosis and monitoring. As wearable devices and mobile monitoring technologies become widespread, ECG data are trending toward diversity and long-term collection, making traditional manual annotation methods inadequate for massive data analysis demands. This research addresses core challenges in ECG signal classification-extremely imbalanced data, significant individual physiological differences, and difficulties in long sequence fitting-by proposing a Principal Component Analysis-based Conditional Generative Adversarial Network (PCA-CGAN). Through in-depth analysis of ECG signal principal component distribution characteristics, we d

SUBMITTER: Tang C 

PROVIDER: S-EPMC12373255 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature