Synthetic Electrocardiogram Spectrogram Generation Using Generative Adversarial Network‐Based Models: A Comparative Study

According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide. The electrocardiogram (ECG) is a widely used noninvasive method for detecting these conditions. However, analyzing long‐duration ECG signal recordings can be highly time‐consuming for medical professionals. Machine learning and deep learning techniques have emerged as valuable tools to assist in diagnosis. However, class imbalance in medical datasets poses a significant challenge. This work presents a comparative analysis of three generative adversarial network (GAN)‐based models—deep convolutional GAN, conditional GAN, and Wasserstein GAN with gradient penalty (WGAN‐GP)—to generate synthetic ECG spectrograms. The proposed models are evaluated using the Fréchet inception distance and structural similarity index measure. The results indicate that WGAN‐GP models outperform the other two models in terms of intraclass diversity and data quality. These findings suggest that GAN‐generated spectrograms can help mitigate data imbalance issues and improve ECG classification models.