论文标题
使用降低铅心电图的混合域自我发项网络，用于多标记心脏不规则性分类
A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram
论文作者
论文摘要
心电图（ECG）通常用于检测心脏纤颤，心动过缓和其他不规则复合物等心脏不规则。尽管先前的研究取得了巨大的成就，可以将这些违规行为归类为标准的12铅ECG，但存在有限的证据，证明了降低了铅ECG在捕获大量诊断信息方面的实用性。此外，分类模型在多个记录源之间的推广性也仍然没有发现。作为2021年心脏病学挑战的生理学计算的一部分，我们的团队Haowan AIEC提出了混合域自我注意解器（MDARSN），以识别降低铅ECG的心脏异常。我们的分类器的得分为0.602、0.593、0.597、0.591和0.589（排名第54，第37、38、38和39位）的得分为12-Lead，6 Lead，6 Lead，4-Lead，4 Lead，3-Lead，3-Lead和2 Lead和2 Lead版本的隐藏验证版本，该验证设置与评估范围的挑战确定为范围。
Electrocardiogram(ECG) is commonly used to detect cardiac irregularities such as atrial fibrillation, bradycardia, and other irregular complexes. While previous studies have achieved great accomplishment classifying these irregularities with standard 12-lead ECGs, there existed limited evidence demonstrating the utility of reduced-lead ECGs in capturing a wide-range of diagnostic information. In addition, classification model's generalizability across multiple recording sources also remained uncovered. As part of the PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC, proposed Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac abnormalities from reduced-lead ECG. Our classifiers received scores of 0.602, 0.593, 0.597, 0.591, and 0.589 (ranked 54th, 37th, 38th, 38th, and 39th) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden validation set with the evaluation metric defined by the challenge.
