Data imbalance in cardiac health diagnostics using CECG-GAN

• Published in: Scientific reports Vol. 14; no. 1; pp. 14767 - 16
• Main Authors: Yang, Yang, Lan, Tianyu, Wang, Yang, Li, Fengtian, Liu, Liyan, Huang, Xupeng, Gao, Fei, Jiang, Shuhua, Zhang, Zhijun, Chen, Xing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/116; 631/114/129; 631/114/1305; 631/114/1314; 631/114/2397; Algorithms; Automation; Cardiac arrhythmia; Cardiology; Cardiovascular disease; Cardiovascular diseases; Civil engineering; Classification; Coronary artery disease; Databases, Factual; Datasets; EKG; Electrocardiogram; Electrocardiography; Electrocardiography - methods; Fatalities; Generative adversarial networks; Heart disease; Heart diseases; Heart Diseases - diagnosis; Heart rate; Humanities and Social Sciences; Humans; Machine learning; Multi-class classification; multidisciplinary; Neural networks; Neural Networks, Computer; Professionals; Science; Science (multidisciplinary); Signal processing; Signal Processing, Computer-Assisted; Unbalanced data; Wavelet transforms; Multi-class classification; Generative adversarial networks; Heart disease; Electrocardiogram; Unbalanced data
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-65619-8

Heart disease is the world’s leading cause of death. Diagnostic models based on electrocardiograms (ECGs) are often limited by the scarcity of high-quality data and issues of data imbalance. To address these challenges, we propose a conditional generative adversarial network (CECG-GAN). This strategy enables the generation of samples that closely approximate the distribution of ECG data. Additionally, CECG-GAN addresses waveform jitter, slow processing speeds, and dataset imbalance issues through the integration of a transformer architecture. We evaluated this approach using two datasets: MIT-BIH and CSPC2020. The experimental results demonstrate that CECG-GAN achieves outstanding performance metrics. Notably, the percentage root mean square difference (PRD) reached 55.048, indicating a high degree of similarity between generated and actual ECG waveforms. Additionally, the Fréchet distance (FD) was approximately 1.139, the root mean square error (RMSE) registered at 0.232, and the mean absolute error (MAE) was recorded at 0.166.