مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 14 54 2026 9 9 ABC-GAN: An Attention-Based Conditional GAN with DTW Loss for ECG Signal Generation to Address Class Imbalance ABC-GAN: An Attention-Based Conditional GAN with DTW Loss for ECG Signal Generation to Address Class Imbalance 79 98 en Parmida Behain دانشگاه الزهرا Noushin Riahi Alzahra University 2025 9 13 <p class="Sammary" style="page-break-after: auto;">The imbalance between normal and pathological cases in Electrocardiogram (ECG) datasets significantly degrades the performance of automated deep learning-based diagnostic systems, particularly for minority classes. This paper introduces ABC-GAN, a novel Attention-Based Conditional Generative Adversarial Network designed to mitigate this critical data imbalance by generating high-fidelity, class-specific synthetic ECG signals. Our proposed model incorporates two key innovations: an attention mechanism within the generator to focus on critical morphological features of the ECG waveform, and the integration of Dynamic Time Warping (DTW) as a distance metric in the generator's loss function to better preserve essential temporal dynamics. Trained and thoroughly evaluated on the MIT-BIH Arrhythmia dataset across seven different heartbeat classes, ABC-GAN successfully generates highly realistic signals that closely match the original data's distribution. When used to augment the training set, these synthetic signals significantly enhance classifier performance and generalization. A downstream classifier achieved an accuracy of 95%, representing a substantial improvement over the baseline trained on the raw imbalanced data and clearly outperforming both standard oversampling techniques and a vanilla Conditional GAN (cGAN). The overall findings demonstrate that ABC-GAN is a powerful and effective tool for data augmentation, fully capable of improving diagnostic accuracy in cardiac research and practical clinical applications.</p> http://jist.ir/en/Article/Download/51397