ABC-GAN: An Attention-Based Conditional GAN with DTW Loss for ECG Signal Generation to Address Class Imbalance
الموضوعات : Signal Processing
Parmida Behain
1
,
Noushin Riahi
2
1 - Department of Computer Engineering, Alzahra University, Tehran
2 - Department of Computer Engineering, Alzahra University, Tehran
الکلمات المفتاحية: ECG Signal Synthesis, Generative Adversarial Network (GAN), Attention Mechanism, Conditional GAN, Dynamic Time Warping (DTW), Class Imbalance, Data Augmentation,
ملخص المقالة :
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.
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