Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), China.
Qilu Hospital of Shandong University, China.
Comput Math Methods Med. 2021 Apr 8;2021:6691177. doi: 10.1155/2021/6691177. eCollection 2021.
Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.
心房颤动（AF）是最常见的心血管疾病之一，具有高致残率和死亡率。早期发现和治疗心房颤动具有重要的临床意义。本文提出了一种多特征融合方法，从单导联短心电图（ECG）记录中筛选出 AF 记录。该方法使用判别典型相关分析（DCCA）特征融合。它充分考虑了类内相关性和类间相关性，并通过简单的串联或并联特征融合解决了计算和信息冗余的问题。DCCA 集成了由专家知识提取的传统特征和由残差网络和门控循环单元网络提取的深度学习特征，以提高单一特征的低准确性。基于 Cardiology Challenge 2017 数据集，设计实验验证所提出算法的有效性。在实验中，F1 指数可达 88%。准确性、敏感性和特异性分别为 91.7%、90.4%和 93.2%。
