Boosting ECG Classification Performance by Pre-training with Synthesized Data

The study presents a novel Gaussian-composition synthesis algorithm for generating synthetic single-lead II ECG data to enhance the classification of abnormal ECGs, specifically targeting atrial fibrillation, atrial flutter, premature ventricular complex, and Wolff-Parkinson-White Syndrome. Evaluating ten different deep neural network architectures, the research shows an average classification performance improvement of 33.2% for atrial flutter when using synthetic data, particularly benefiting scenarios with limited real-world datasets. This approach highlights the potential of domain-specific synthetic data as a pre-training resource, addressing data scarcity challenges in medical applications.