Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Physiol Meas. 2011 Aug;32(8):1147-62. doi: 10.1088/0967-3334/32/8/010. Epub 2011 Jun 27.
Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L(max )), average length of the diagonal lines (L(mean)), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches.
心房颤动（AF）是最常见的心律失常，会增加中风的风险。基于非侵入性技术预测阵发性心房颤动（PAF）的发作具有重要的临床意义，可以避免不必要的治疗干预，并最大程度地降低患者的风险。在本文中，我们提出了一种基于 RR 间隔信号分析的有效 PAF 预测器。该方法包括三个步骤：预处理、特征提取和分类。在第一步中，从心电图（ECG）信号中检测到 QRS 复合体，然后提取 RR 间隔信号。在下一步中，获得 RR 间隔信号的递归图（RP），并提取五个统计显著的特征来描述 RP 的基本模式。这些特征包括递归率、最长对角线段的长度（L(max)）、对角线的平均长度（L(mean)）、熵和捕获时间。递归量化分析可以揭示其他方法不易察觉的动力学细微方面，并表现出由典型动力学行为引起的特征模式。在最后一步中，使用基于支持向量机（SVM）的分类器进行 PAF 预测。使用包含 PAF 发作前结束的 30 分钟 ECG 记录和至少 45 分钟远离任何 PAF 事件的片段的心房颤动预测数据库（AFPDB）评估所提出方法在预测 PAF 发作中的性能。获得的敏感性、特异性、阳性预测值和阴性预测值分别为 97%、100%、100%和 96%。所提出的方法比其他现有方法具有更好的结果。
