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.

中文翻译：

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使用判别典型相关分析基于特征融合的自动房颤检测

心房颤动（AF）是最常见的心血管疾病之一，具有很高的致残率和死亡率。房颤的早期发现和治疗具有重要的临床意义。在本文中，提出了一种多特征融合来从单导联短心电图 (ECG) 记录中筛选出 AF 记录。所提出的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，通过简单的串行或并行特征融合来解决计算和信息冗余问题。DCCA 将传统的专家知识提取的特征与残差网络和门控循环单元网络提取的深度学习特征相结合，以改善单个特征的低准确率。基于 Cardiology Challenge 2017 数据集，实验旨在验证所提出算法的有效性。在实验中，F1指标可以达到88%。准确度、灵敏度和特异性分别为 91.7%、90.4% 和 93.2%。