Automatic Real Time Detection of Atrial Fibrillation
Automatic Real Time Detection of Atrial Fibrillation
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DOI：
10.1007/s10439-009-9740-z
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发表时间：
2009-09-01
影响因子：
3.8
通讯作者：
Raeder, E. A.
中科院分区：
文献类型：
--
作者：
Dash, S.;Chon, K. H.;Raeder, E. A.
心房颤动（AF）是最常见的持续性心律失常，与显著的发病率和死亡率相关。由于患者可能无症状，因此难以及时诊断心律失常，特别是短暂发作。在这项研究中，我们描述了一个强大的算法自动检测AF的基础上的随机性，变异性和复杂性的心跳间隔（RR）时间序列。具体而言，我们采用了一种新的统计，转折点比，结合连续RR差异的均方根和香农熵来表征这种心律失常。检测算法在两个数据库上进行了测试，即MIT-BIH房颤数据库和MIT-BIH心律失常数据库。这些数据库包含来自大量房颤和非房颤患者的几个长RR间期系列，其中一些数据包含各种形式的异位搏动。通过使用受试者工作特征（ROC）曲线确定的阈值和数据段长度，我们实现了高灵敏度和特异性（MIT-BIH房颤数据库分别为94.4%和95.1%）。即使在MIT-BIH心律失常数据库中针对AF与其他几种潜在混杂心律失常进行测试时，该算法也表现良好（灵敏度= 90.2%，特异性= 91.2%）。
Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with significant morbidity and mortality. Timely diagnosis of the arrhythmia, particularly transient episodes, can be difficult since patients may be asymptomatic. In this study, we describe a robust algorithm for automatic detection of AF based on the randomness, variability and complexity of the heart beat interval (RR) time series. Specifically, we employ a new statistic, the Turning Points Ratio, in combination with the Root Mean Square of Successive RR Differences and Shannon Entropy to characterize this arrhythmia. The detection algorithm was tested on two databases, namely the MIT-BIH Atrial Fibrillation Database and the MIT-BIH Arrhythmia Database. These databases contain several long RR interval series from a multitude of patients with and without AF and some of the data contain various forms of ectopic beats. Using thresholds and data segment lengths determined by Receiver Operating Characteristic (ROC) curves we achieved a high sensitivity and specificity (94.4% and 95.1%, respectively, for the MIT-BIH Atrial Fibrillation Database). The algorithm performed well even when tested against AF mixed with several other potentially confounding arrhythmias in the MIT-BIH Arrhythmia Database (Sensitivity = 90.2%, Specificity = 91.2%).