Abstract
The electrocardiogram (ECG) is a vital diagnostic tool used to monitor and diagnose a wide range of cardiac conditions. However, ECG signals can exhibit significant variability across different patient populations, recording devices, and environmental conditions, creating challenges in developing universally robust and accurate classification models. This research addresses these challenges by exploring and advancing domain adaptation techniques to enhance the robustness and generalizability of ECG classification models. By leveraging unsupervised domain adaptation (UDA), we aim to mitigate the performance degradation that typically occurs when models trained on one dataset are applied to another, thereby improving diagnostic accuracy and reliability across diverse clinical settings. We introduce ECG-Adapt, an integrated approach that aligns features both within classes and across domains. Unlike existing methods that rely on clustering as a preprocessing step, ECG-Adapt does not require clustering, simplifying the workflow. It further incorporates weakly supervised learning to prevent overfitting of the discriminator to pseudo-labels generated by the classifier, enhancing robustness and generalizability. Applying our novel unsupervised domain adaptation framework led to substantial performance gains. For instance, ECG-Adapt improved the average $F_{1}$-score by 8% on single-lead problems and 7% on 12-lead problems. By leveraging ECG-Adapt, performance degradation when applying models across datasets can be mitigated, enhancing diagnostic accuracy and reliability in diverse clinical settings and demonstrating strong potential for real-world deployment.