Atrial fibrillation (AF) is the most common cardiac arrhythmia and shows a rising trend with the increase of aged. Currently, existing intelligent AF detection methods have achieved good results in massive labeled data. However, it is time-consuming and undesirable to label ECG signals in real applications. Meanwhile, due to distribution discrepancy by different testing conditions, it is unsatisfied for directly applying trained model to other datasets. Inspired by the domain adaptation techniques, this paper proposes a novel Domain Adaptive Residual Network (DARN) to detect AF of unlabeled datasets with the aid of detection knowledge of labeled dataset. Firstly, residual blocks are adopted to extract informative deep features from the ECG signals automatically. Then, deep features are fed into feature classifier to acquire final detection result. Further, the multi-layer multi-kernel maximum mean discrepancy is combined into the training process to reduce distribution discrepancy of different domains, which imposes constraints on network parameters. Finally, the proposed method was evaluated with the data from MIT-BIH Atrial Fibrillation Database (AFDB), MIT-BIH Arrhythmia Database and 2017 Physionet challenge dataset. The experimental results show that the proposed domain adaptive approach improves the accuracy by 4.50% on average and the F1 score by 4.28% on average using the knowledge of AFDB. Additionally, comparison experiment shows that the proposed feature extractor and classifier achieved 98.97%, 98.75%, and 98.84% for the sensitivity, specificity, and accuracy on the AFDB, respectively. Consequently, the proposed method is provided with high application potential as a valuable auxiliary tool for clinical AF detection.

心房纤颤（AF）是最常见的心律不齐，并且随着年龄的增长呈上升趋势。当前，现有的智能AF检测方法在海量标记数据中已经取得了良好的效果。然而，在实际应用中标记ECG信号是费时的并且是不希望的。同时，由于不同测试条件下的分布差异，将训练后的模型直接应用于其他数据集并不令人满意。受到领域自适应技术的启发，本文提出了一种新颖的领域自适应残差网络（DARN），可以借助标记数据集的检测知识来检测未标记数据集的AF。首先，采用残差块自动从ECG信号中提取信息丰富的深度特征。然后，将深层特征馈入特征分类器以获得最终检测结果。此外，将多层多内核最大平均差异合并到训练过程中，以减少不同域的分布差异，这对网络参数施加了约束。最后，使用MIT-BIH心房颤动数据库（AFDB），MIT-BIH心律失常数据库和2017年Physionet挑战数据集的数据对提出的方法进行了评估。实验结果表明，利用AFDB的知识，提出的域自适应方法平均可将准确性提高4.50％，将F1评分平均提高4.28％。另外，对比实验表明，所提出的特征提取器和分类器在AFDB上的敏感性，特异性和准确性分别达到98.97％，98.75％和98.84％。所以，