Revolutionizing Cardiac Care with Regenerative AI and GANs

Abstract

The human heart\'s limited regenerative capacity poses significant challenges in treating myocardial infarctions and other cardiac diseases. Traditional therapeutic approaches, such as medications, stents, and transplants, primarily focus on symptom management rather than reversing underlying myocardial damage. Recent breakthroughs in regenerative artificial intelligence, particularly through the use of generative adversarial networks, offer promising avenues for cardiac repair. GANs have been successfully applied in cardiovascular research to enhance imaging analysis and simulate realistic data, improving diagnostic accuracy and treatment outcomes. Building on this foundation, we propose a novel approach to cardiac signal regeneration using GAN-based AI. This methodology enables the reconstruction and enhancement of degraded or missing electrocardiogram signals, significantly improving diagnostic accuracy. Moreover, it opens new possibilities for AI-assisted myocardial tissue regeneration by predicting and simulating healthy cardiac patterns. These regenerated signals can be integrated into pacemakers and other cardiac devices to optimize pacing and improve heart function. By leveraging AI-driven insights, this approach not only enhances diagnostic capabilities but also paves the way for personalized, data-driven interventions that transcend traditional symptomatic management, potentially revolutionizing cardiac care and offering new hope for patients with cardiac diseases.

Introduction

1. Introduction

• Cardiac diseases are a leading cause of death globally.

• Current treatments (medications, stents, transplants) manage symptoms but do not regenerate heart tissue.

• The heart’s limited ability to self-repair drives the need for regenerative strategies.

• Generative Adversarial Networks (GANs) offer potential in biomedical applications such as:

  • ECG signal enhancement

  • Simulation of healthy cardiac patterns

  • Tissue regeneration modeling

2. Research Objectives

• Develop a custom GAN architecture to:

  • Reconstruct degraded ECG signals

  • Simulate healthy cardiac electrical activity

  • Improve diagnostic accuracy and enable AI-assisted myocardial tissue regeneration

• Explore using regenerated ECG signals to optimize pacemaker function.

3. Research Contributions

• Proposes GAN-enhanced ECG signal regeneration for cardiac diagnostics.

• Introduces AI-driven pacing strategies using simulated healthy ECG signals.

• Shows improved diagnostic precision over traditional signal classification methods.

• Aims to shift from symptomatic to regenerative, personalized cardiac care.

4. Literature Review Highlights

• Traditional ECG analysis via CNNs and RNNs is limited to classification and denoising.

• Advanced techniques (e.g., wavelet transforms, hybrid RNNs) have explored signal feature extraction.

• GANs have recently been used to:

  • Generate realistic synthetic ECGs

  • Support data augmentation

  • Enhance diagnostic model training

• Few studies focus on regeneration and real-time applications of ECGs.

5. Methodology

Dataset

• MIT-BIH Arrhythmia Dataset used for training and testing.

• Data underwent:

  • Noise reduction

  • Normalization

  • Segmentation

  • Outlier removal and interpolation

GAN Architecture (ReGen-ECGNet)

• 1D GAN with:

  • Generator: Reconstructs high-fidelity ECG signals

  • Discriminator: Differentiates real from generated ECGs

• Uses adversarial and reconstruction losses for training

• Performance metrics: RMSE, PSNR, SSIM

Signal Simulation & Application

• Reconstructed signals simulate healthy myocardial activity

• Can be integrated into AI-assisted pacemaker algorithms

• Aims to improve pacing by aligning with natural heart rhythms

Ethical Considerations

• Privacy, data anonymization, and regulatory compliance are emphasized

• Follows ethical AI standards for transparency, reproducibility, and patient safety

6. Results & Evaluation

• GAN model outperforms CNN, RNN, and LSTM in reconstructing and classifying ECGs.

• Best performance in accuracy and fidelity of ECG regeneration.

• Demonstrates GAN’s potential for:

  • Enhancing ECG signal quality

  • Supporting regenerative cardiac therapy

7. Dataset & Class Distribution

• Heartbeats categorized per AAMI standards into:

  • NB (Normal): 90,589

  • SB (Supraventricular): 8,039

  • VB (Ventricular): 7,236

  • FB (Fusion): 2,776

  • QB (Unknown/Noise): 803

• Training/Test Split: 70% training / 30% testing for each class

Conclusion

The capabilities of Generative Adversarial Networks for ECG signal regression and classification in the area of cardiac care are shown in this study. In comparison to traditional model such as CNN, RNN and LSTM GAN based approach demonstrated significance in metrics which validates that GAN have potential enhancement to the diagnostic capability by reconstructing degraded ECG signals. An attempt at advancing real time cardiac monitoring and personalized treatment can be made by combining the AI driven insights. The future research will further improve the GAN architecture for even better signal regeneration by experimenting with such advanced variations of the GANs as WGANs and cGANs to be more stable and efficient. Moreover, the application of this approach to wearable devices and integration of such in AI-based cardiac devices could enhance the heart function. Another direction of future work is to further validate the large-scale clinical applicability using real world healthcare settings for ensuring the practical applicability and safety of AI assisted cardiac care.

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Copyright

Copyright © 2025 Ashia Monachan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.