In the presence of premature atrial contraction (PAC), premature ventricular contraction (PVC) or other ectopic beats, RR intervals (RRIs) may be disturbed, which results in other types of heart disease being misdiagnosed as atrial fibrillation (AF). In this study, a low-complexity AF detection method based on short ECG is proposed, which includes RRIs modification and feature selection. The extracted RRIs are used to determine whether the potential RRI interference exists and to modify it. Next, based on the modified RRIs, the features are evaluated and selected by the methods of correlation criterion, Fisher criterion, and minimum redundancy maximum relevance criterion. Finally, filtered features are classified by the artificial neural network (ANN). The algorithm is validated in a test set including 2332 AF, 313 normal (NOR), 239 atrioventricular block (IAVB), 81 left bundle branch block (LBBB), 624 right bundle branch block (RBBB), 426 PAC and 564 PVC. Compared with the previous detection method of AF based on the RRIs, the proposed method achieved an overall sensitivity of 94.04% and an overall specificity of 86.74%. The specificity of the test set containing only AF and NOR is up to 99.04%. Meanwhile, the overall false-positive rate (FPR) of PAC and PVC can be reduced by 9.19%. While ensuring accuracy, this method effectively reduces the probability of misdiagnosis of PVC and PAC as AF. It is an automatic detection method of AF suitable for inter-patient clinical short-term ECG.

中文翻译：

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使用短期心电图检测心房颤动的基于特征选择的算法

在存在房性早搏 (PAC)、室性早搏 (PVC) 或其他异位搏动的情况下，RR 间期 (RRI) 可能会受到干扰，从而导致其他类型的心脏病被误诊为心房颤动 (AF)。在这项研究中，提出了一种基于短心电图的低复杂度AF检测方法，包括RRIs修改和特征选择。提取的 RRI 用于确定是否存在潜在的 RRI 干扰并对其进行修改。接下来，基于修改后的RRI，通过相关性准则、Fisher准则和最小冗余最大相关性准则的方法对特征进行评估和选择。最后，过滤后的特征由人工神经网络（ANN）分类。该算法在包括 2332 AF、313 正常 (NOR)、239 例房室传导阻滞 (IAVB)、81 例左束支传导阻滞 (LBBB)、624 例右束支传导阻滞 (RBBB)、426 例 PAC 和 564 例 PVC。与以往基于 RRI 的 AF 检测方法相比，该方法的总体敏感性为 94.04%，总体特异性为 86.74%。仅包含 AF 和 NOR 的测试集的特异性高达 99.04%。同时，PAC和PVC的总体假阳性率（FPR）可降低9.19%。该方法在保证准确性的同时，有效降低了PVC和PAC误诊为AF的概率。是一种适用于患者间临床短期心电图的AF自动检测方法。所提出的方法实现了 94.04% 的总体灵敏度和 86.74% 的总体特异性。仅包含 AF 和 NOR 的测试集的特异性高达 99.04%。同时，PAC和PVC的总体假阳性率（FPR）可降低9.19%。该方法在保证准确性的同时，有效降低了PVC和PAC误诊为AF的概率。是一种适用于患者间临床短期心电图的AF自动检测方法。所提出的方法实现了 94.04% 的总体灵敏度和 86.74% 的总体特异性。仅包含 AF 和 NOR 的测试集的特异性高达 99.04%。同时，PAC和PVC的总体假阳性率（FPR）可降低9.19%。该方法在保证准确性的同时，有效降低了PVC和PAC误诊为AF的概率。是一种适用于患者间临床短期心电图的AF自动检测方法。