GAEA-Net: Generating Activity-Enriched Abnormal ECGs via Adversarial Network

With the increasing demand for personalized health monitoring through wearable devices, there is a growing need for non-prescription ECG diagnosing, especially during physical activities. However, existing abnormal ECG data are typically measured in clinical settings, reflecting heart waveforms in a resting state. Abnormality classification models based on such data often struggle to maintain high performance during physical activities, leading to increased false alarms and a higher incidence of missed detections. Due to the potential risks associated with having patients engage in physical activity, abnormal ECG data captured during exercise is not readily available, further complicating the development of reliable models for active scenarios. To address this issue, we propose GAEA-Net in this study. Our goal is to utilize exercise ECGs from healthy individuals, which are more easily accessible, combined with resting-state abnormal ECGs, to generate activity-enriched ECGs through synthesis. We conduct abnormal classification on five widely used datasets, achieving average improvements of 1.3% in Accuracy, 1.3% in F1-score, 0.9% in AUROC, 1.6% in MCC, and 1.4% in Cohen's Kappa. Furthermore, a clinical Turing test involving seven experienced cardiologists confirms that our synthesized ECGs exhibit high fidelity. In the diagnostic task, the cardiologists achieved comparable accuracy on synthetic and real ECGs (55.7% vs. 54.9%, p = 0.76).