Introducing a Comprehensive Framework for Synthetic ECG Validation.

Goal: While Generative AI addresses data scarcity and privacy in healthcare, the lack of standardized validation protocols hinders the deployment of safe and trustworthy AI. This study introduces a comprehensive framework for rigorously evaluating the authenticity and fidelity of synthetic biosignals, addressing the critical need for medical data quality assurance.

Methods

The framework was validated using synthetic electrocardiograms (ECG) for Brugada Syndrome generated via Generative Adversarial Networks (GANs). The protocol integrates four complementary analytics layers: (1) visual morphological inspection; (2) statistical assessment of clinical biomarkers (e.g., QT-interval); (3) computational similarity metrics (e.g. MSE, DTW) to assess distributional fidelity; and (4) a human-centered validation where blinded expert cardiologist scored clinical realism.

Results

Results demonstrated high statistical fidelity and morphological realism. Quantitative analysis showed no statistically significant differences in key clinical features between real and synthetic distributions. An expert cardiologist achieved near 50% accuracy (random chance) in distinguishing real from synthetic signals, consistent with the high authenticity of the generated data.

Conclusions

This framework proposes a comprehensive methodology for validating generative healthcare models, intended as an initial step toward standardization in the field. By ensuring high-fidelity data generation, this approach supports robust predictive modeling and AI-based decision support, facilitating privacy-sensitive clinical research.