Insights in Data Generation: A Synthetic Data Approach for Enabling Small Datasets in Atrial Fibrillation Research

This study explores the Gaussian Copula Synthesizer’s (GCS) utility in addressing the limitations of a small dataset (58 real patient records) in Atrial Fibrillation (AF) research, focusing on Heart Rate Variability (HRV). Leveraging this method, we generated a realistic synthetic dataset of 1, 000 records, replicating the features observed in the original records. The GCS effectively expands dataset size while maintaining HRV pattern realism. This aids in developing and refining models used in AF research, overcoming challenges associated with limited sample sizes. Emphasizing privacy considerations, this approach showcases the potential of classic statistical methods in synthetic data generation for advancing AF research within the constraints of small datasets.