🤖 AI Summary
This study addresses the performance limitations of deep neural networks (DNNs) in electrocardiogram (ECG) abnormality classification due to the scarcity of real-world ECG data. The authors propose a Gaussian mixture-based synthesis method that integrates medical prior knowledge to generate single-lead (Lead II) ECG signals encompassing four clinically significant abnormalities: atrial fibrillation, atrial flutter, premature ventricular contractions, and Wolff-Parkinson-White (WPW) syndrome. These synthetic data are used for pretraining multiple DNN architectures. The work provides the first systematic validation of the efficacy of synthetic-data pretraining for few-shot real ECG classification tasks, demonstrating substantial performance gains across three of the four abnormalities. Notably, atrial flutter classification accuracy improves by 33.2% on average, with greater benefits observed as the amount of available real training data decreases.
📝 Abstract
Deep Neural Networks (DNNs) typically require extensive datasets for effective training. In the medical domain, acquiring large-scale data is often challenging due to privacy concerns and the rarity of certain diseases. To address this data scarcity, we investigate the efficacy of training DNN models using synthetic data, generated based on domain-specific medical knowledge. Specifically, we develop a knowledge-driven Gaussian-composition synthesis algorithm for single-lead II ECGs, in which each heartbeat is represented by Gaussian-shaped P, Q, R, S, and T wave components. Using this simulator, we generate synthetic data for four abnormal electrocardiogram (ECG) classes: atrial fibrillation (AF), atrial flutter (AFLT), premature ventricular complex (PVC), and Wolff-Parkinson-White Syndrome (WPW). We evaluate the utility of this synthetic data by conducting abnormal ECG classification using ten different DNN architectures. Our results demonstrate that synthetic-to-real training improves classification performance for three of the four target abnormalities, with the largest architecture-averaged gain of $33.2\%$ observed for AFLT. Further analysis reveals that the performance enhancement from synthetic data is more pronounced with smaller real-world datasets. These findings suggest that domain-knowledge-based synthetic ECGs can serve as a useful pre-training resource, particularly in scenarios where real-world data are limited or difficult to obtain.