Adaptive Region Aggregation Network: Unsupervised Domain Adaptation With Adversarial Training For Ecg Delineation

Electrocardiogram (ECG) delineation, which provides clinically useful information for the diagnosis of cardiovascular disease, is an essential task in automated ECG analysis. The discrepancies among ECG signals from different datasets, namely domain shifts, may bring severe challenges to the cross-dataset performance of ECG delineation algorithms. The domain shifts are generally caused by the differences of conditions, collecting devices, and individual characteristics, and are inherent and non-negligible in ECG. In this work, we propose an unsupervised domain adaptation method called Adaptive Region Aggregation Network (ARAN) based on adversarial training to tackle domain shift problem in ECG delineation. The proposed algorithm promotes the state-of-the-art deep neural network RAN to learn domain-invariant features and achieve improving performance on both source and target domain. The experiments results on two public datasets, LUDB and QT database, prove that our approach can effectively improve the cross-dataset performance of the state-of-the-art deep learning model.