Introducing a Comprehensive Framework for Synthetic ECG Validation: Application to Brugada Syndrome

Abstract

Rare cardiac disease research faces significant challenges due to limited data availability and accessibility. Recent advancements in synthetic data generation may be a cornerstone in overcoming urgent data needs. The present study presents a framework for synthetic ECG generation and evaluation and applies it to the case of Brugada Syndrome. A synthetic ECG dataset representative of Brugada type I patients is produced by leveraging a state-of-the-art generative adversarial network originally designed for normal ECG synthesis. A comprehensive evaluation procedure for synthetic biosignals is introduced. It includes visual inspection, ECG characteristic evaluation, established similarity metrics, and expert cardiologist scoring of both synthetic and real datasets. The evaluation of the synthetic Brugada dataset has shown optimal results, with less than 50% accuracy regarding cardiologists' scoring. These outcomes highlight the enormous potential of synthetic data generation techniques and the absolute need for a more standardized generation and evaluation process.