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.