Parvaneh Saman, Golpayegani Mohammad Reza Hashemi, Firoozabadi Mohammad, Haghjoo Majid
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran.
Proc Inst Mech Eng H. 2012 Jan;226(1):3-20. doi: 10.1177/0954411911425839.
Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space. The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge 2004 was applied in the present study. It includes one training dataset and two testing datasets, A and B. The present investigation was initiated by producing a two-dimensional reconstructed phase space (RPS) of the ECG. Then, a Poincare line was drawn in a direction that included the maximum point distribution in the RPS and also passed through the origin of the RPS coordinate system. Afterward, the coordinates of the RPS trajectory intersections with this Poincare line were extracted to capture the local behavior related to the arrhythmia under investigation. The POS corresponding to atrial activity were selected with regard to the fact that similar ECG morphologies such as P waves, which are corresponding to atrial activity, distribute in a specific region of the RPS. Thirteen features were extracted from the selected intersection points to quantify their distributions. To select the best feature subset, a genetic algorithm (GA), in combination with a support vector machine (SVM), was applied to the training dataset. Based on the selected features and trained SVM, the performance of the proposed method was evaluated using the testing datasets. The results showed that 86.67% of dataset A and 80% of dataset B were correctly classified. This classification accuracy is in the same range as or higher than that of recent studies in this area. These results show that the proposed method, in which no complicated QRST cancelation algorithm was used, has the potential to predict AF termination.
心房颤动（AF）是一种常见的心律失常。预测AF自发终止的条件是一项重要任务，这将给患者和临床医生都带来极大益处。在本研究中，提出了一种新方法，通过利用心电图（ECG）相空间中庞加莱截面的截面点（POS）坐标来预测AF的自发终止。本研究应用了PhysioNet为2004年心脏病学计算机挑战赛提供的AF终止数据库。它包括一个训练数据集和两个测试数据集A和B。本研究首先生成ECG的二维重构相空间（RPS）。然后，在包含RPS中最大点分布且经过RPS坐标系原点的方向上绘制一条庞加莱线。之后，提取RPS轨迹与该庞加莱线交点的坐标，以捕捉与所研究心律失常相关的局部行为。考虑到与心房活动相对应的类似ECG形态（如P波）分布在RPS的特定区域这一事实，选择与心房活动相对应的POS。从选定的交点提取了13个特征以量化其分布。为了选择最佳特征子集，将遗传算法（GA）与支持向量机（SVM）相结合应用于训练数据集。基于选定的特征和训练好的SVM，使用测试数据集评估所提方法的性能。结果表明，数据集A的86.67%和数据集B的80%被正确分类。该分类准确率与该领域近期研究的准确率处于相同范围或更高。这些结果表明，所提方法在未使用复杂的QRST消除算法的情况下，具有预测AF终止的潜力。
